"
+ ],
+ "text/plain": [
+ " 0 1 2 3 4 5 6 7 8 \\\n",
+ "0 0.0 0.0 1.0 0.0 202500.0 406597.5 24700.5 351000.0 0.018801 \n",
+ "1 0.0 0.0 0.0 0.0 270000.0 1293502.5 35698.5 1129500.0 0.003541 \n",
+ "2 1.0 1.0 1.0 0.0 67500.0 135000.0 6750.0 135000.0 0.010032 \n",
+ "3 0.0 0.0 1.0 0.0 135000.0 312682.5 29686.5 297000.0 0.008019 \n",
+ "4 0.0 0.0 1.0 0.0 121500.0 513000.0 21865.5 513000.0 0.028663 \n",
+ "\n",
+ " 9 ... 229 230 231 232 233 234 235 236 237 238 \n",
+ "0 9461.0 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 \n",
+ "1 16765.0 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 \n",
+ "2 19046.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "3 19005.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "4 19932.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ "[5 rows x 239 columns]"
+ ]
+ },
+ "execution_count": 237,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 249,
"id": "f2282a63",
"metadata": {},
"outputs": [],
@@ -2936,14 +3592,18 @@
" df = X_train.copy()\n",
" df.columns = feature_names['0'].tolist()\n",
" df = df[SHAP_feature_important]\n",
+ " # Remove all column names\n",
+ " #df.rename(columns={x:y for x,y in zip(df.columns,range(0,len(df.columns)))})\n",
+ " df.columns = [x for x in range(0, len(df.columns))] \n",
" print(df.shape)\n",
- " print(df.info())\n",
+ " #print(df.info())\n",
+ " print(df.head())\n",
" return df"
]
},
{
"cell_type": "markdown",
- "id": "0eb0cd93",
+ "id": "b7b2f0ca",
"metadata": {},
"source": [
"### First attempt to improve feature selection and model training"
@@ -2977,13 +3637,13 @@
}
],
"source": [
- "new_X_train = select_columns(X_train, feature_names, SHAP_feature_important)\n",
- "new_X_test = select_columns(X_test, feature_names, SHAP_feature_important)"
+ "new_X_train = select_columns(X_train, feature_names, shap_df, 0.001)\n",
+ "new_X_test = select_columns(X_test, feature_names, shap_df, 0.001)"
]
},
{
"cell_type": "code",
- "execution_count": 203,
+ "execution_count": 234,
"id": "e2578818",
"metadata": {},
"outputs": [
@@ -2991,7 +3651,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "START time Fri Mar 1 11:46:14 2024\n"
+ "START time Sat Mar 2 18:28:36 2024\n"
]
},
{
@@ -3027,6 +3687,44 @@
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
@@ -3099,6 +3797,74 @@
"text": [
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
@@ -3175,6 +3941,108 @@
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
@@ -3289,6 +4157,7 @@
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
+ "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
@@ -3356,7 +4225,13 @@
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
+ " return fit_method(estimator, *args, **kwargs)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
@@ -3366,13 +4241,7 @@
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
+ " return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
@@ -3503,10 +4372,6 @@
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n"
]
},
@@ -3583,6 +4448,12 @@
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n",
"/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return fit_method(estimator, *args, **kwargs)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
" return fit_method(estimator, *args, **kwargs)\n"
]
},
@@ -3663,223 +4534,460 @@
]
},
{
- "name": "stderr",
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Best Hyperparameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
+ "START time Sat Mar 2 18:28:36 2024\n",
+ "END time Sat Mar 2 22:15:31 2024 duration 226.91404071648915 min\n",
+ "\n",
+ "---------------------------------\n",
+ "start generate_model_report\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "+------------------+---------------------+---------------------+\n",
+ "| Confusion Matrix | Positive prediction | Negative prediction |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "| Positive class | True positive (TP) | False negative (FN) |\n",
+ "| Negative class | False positive (FP) | True negative (TN) |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "+------------------+---------------------+---------------------+\n",
+ "| Confusion Matrix | Positive prediction | Negative prediction |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "| Positive class | 4 | 4880 |\n",
+ "| Negative class | 3 | 56616 |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "ROC AUC: 0.5003830075360833\n",
+ "Accuracy = 0.920605498918752\n",
+ "Precision = 0.5714285714285714\n",
+ "Recall = 0.000819000819000819\n",
+ "F1 Score = 0.0016356573297894093\n",
+ "Fbeta Score = (0.49, 0.92, 0.91)\n",
+ " model tn fp fn tp FP+10*FN accuracy ROC_AUC \\\n",
+ "0 RFC_newFEATURE_001 56616 3 4880 4 48803 0.920605 0.500383 \n",
+ "\n",
+ " precision recall F1_Score Fbeta_macro Fbeta_micro Fbeta_weighted \n",
+ "0 0.571429 0.000819 0.001636 0.49 0.92 0.91 \n",
+ "---------------------------------\n",
+ "start find_optimal_business_score\n",
+ "prediction proba 61503\n",
+ "Y_true 61503\n",
+ "Series([], Name: best, dtype: object)\n",
+ "0 1\n",
+ "Name: best, dtype: object\n",
+ "best b score 36741 1 0.1\n",
+ "Name: threshold, dtype: float64\n",
+ " threshold tn fp fn tp FP+10*FN accuracy ROC_AUC \\\n",
+ "0 0.0 0 56619 0 4884 56619 0.079411 0.500000 \n",
+ "1 0.1 36738 19881 1686 3198 36741 0.649334 0.651827 \n",
+ "2 0.2 52686 3933 3638 1246 40313 0.876900 0.592827 \n",
+ "3 0.3 55991 628 4521 363 45838 0.916281 0.531616 \n",
+ "4 0.4 56550 69 4827 57 48339 0.920394 0.505226 \n",
+ "5 0.5 56614 5 4879 5 48795 0.920589 0.500468 \n",
+ "\n",
+ " precision recall F1_Score Fbeta_macro Fbeta_micro Fbeta_weighted \\\n",
+ "0 0.079411 1.000000 0.147137 0.150668 0.079411 0.023929 \n",
+ "1 0.138568 0.654791 0.228731 0.534326 0.649334 0.668162 \n",
+ "2 0.240587 0.255119 0.247640 0.591790 0.876900 0.877552 \n",
+ "3 0.366297 0.074324 0.123574 0.531957 0.916281 0.905051 \n",
+ "4 0.452381 0.011671 0.022754 0.498384 0.920394 0.905420 \n",
+ "5 0.500000 0.001024 0.002043 0.492133 0.920589 0.905030 \n",
+ "\n",
+ " best \n",
+ "0 0 \n",
+ "1 1 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 \n",
+ "5 0 \n",
+ "Artifact PATH RFC_newFEATURE_001_artifactPATH\n",
+ "{'TN': 36738, 'FP': 19881, 'FN': 1686, 'TP': 3198, 'FP_10_FN': 36741, 'Accuracy': 0.6493341788205453, 'F1': 0.2287308228730823, 'Precision': 0.138567528922397, 'Recall': 0.6547911547911548, 'ROC_AUC': 0.6518273052607817, 'threshold': 0.1, 'time_in_s': 13614.84244298935}\n",
+ "{'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
+ "Active run_id: ce6238f4e7664792abd37182bebc6061\n"
+ ]
+ }
+ ],
+ "source": [
+ "run_name = \"RFC_newFEATURE_001\"\n",
+ "RFC_model_001, best_RFC_params, time_RFC = RFC_model(new_X_train, Y_train)\n",
+ "RFC_metrics, best_metrics_RFC = generate_model_report(RFC_model_001, run_name, new_X_test, Y_test, time_RFC)\n",
+ "run_MLflow(experiment_name, run_name, RFC_metrics, \n",
+ " best_RFC_params, RFC_model_001, new_X_train)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ce809a7d",
+ "metadata": {},
+ "source": [
+ "### Second attempt to improve feature selection and model improvement"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 253,
+ "id": "c1d66850",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "length important features 96\n",
+ "(246008, 96)\n",
+ " 0 1 2 3 4 5 6 7 8 \\\n",
+ "0 0.0 0.0 1.0 0.0 202500.0 406597.5 24700.5 351000.0 0.018801 \n",
+ "1 0.0 0.0 0.0 0.0 270000.0 1293502.5 35698.5 1129500.0 0.003541 \n",
+ "2 1.0 1.0 1.0 0.0 67500.0 135000.0 6750.0 135000.0 0.010032 \n",
+ "3 0.0 0.0 1.0 0.0 135000.0 312682.5 29686.5 297000.0 0.008019 \n",
+ "4 0.0 0.0 1.0 0.0 121500.0 513000.0 21865.5 513000.0 0.028663 \n",
+ "\n",
+ " 9 ... 86 87 88 89 90 91 92 93 94 95 \n",
+ "0 9461.0 ... 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 \n",
+ "1 16765.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0 \n",
+ "2 19046.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "3 19005.0 ... 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "4 19932.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ "[5 rows x 96 columns]\n",
+ "length important features 96\n",
+ "(61503, 96)\n",
+ " 0 1 2 3 4 5 6 7 8 9 \\\n",
+ "0 0 0 1 1 180000.0 545040.0 36553.5 450000.0 0.010643 15037 \n",
+ "1 0 1 1 1 337500.0 790830.0 62613.0 675000.0 0.010006 13347 \n",
+ "2 0 0 1 1 63000.0 310500.0 15232.5 310500.0 0.026392 16263 \n",
+ "3 0 0 0 0 112500.0 942300.0 36643.5 675000.0 0.072508 16629 \n",
+ "4 0 1 1 0 180000.0 272520.0 19957.5 225000.0 0.008575 10763 \n",
+ "\n",
+ " ... 86 87 88 89 90 91 92 93 94 95 \n",
+ "0 ... False False False False False False True True True False \n",
+ "1 ... False True False False False False True False True False \n",
+ "2 ... False False False False True False False False False False \n",
+ "3 ... False True False False True False True False True False \n",
+ "4 ... False False False False False False False False False False \n",
+ "\n",
+ "[5 rows x 96 columns]\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'MinMaxScaler' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[253], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m new_X_train_002 \u001b[38;5;241m=\u001b[39m select_columns(X_train, feature_names, shap_df, \u001b[38;5;241m0.002\u001b[39m)\n\u001b[1;32m 2\u001b[0m new_X_test_002 \u001b[38;5;241m=\u001b[39m select_columns(X_test, feature_names, shap_df, \u001b[38;5;241m0.002\u001b[39m)\n\u001b[0;32m----> 4\u001b[0m X_train_002_scaled, X_test_002_scaled \u001b[38;5;241m=\u001b[39m \u001b[43mscale_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_X_train_002\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_X_test_002\u001b[49m\u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
+ "Cell \u001b[0;32mIn[252], line 3\u001b[0m, in \u001b[0;36mscale_data\u001b[0;34m(df_train, df_test)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mscale_data\u001b[39m(df_train, df_test):\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# Scale the domainnomial features\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m scaler \u001b[38;5;241m=\u001b[39m \u001b[43mMinMaxScaler\u001b[49m(feature_range \u001b[38;5;241m=\u001b[39m (\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m))\n\u001b[1;32m 5\u001b[0m df_train \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mfit_transform(df_train)\n\u001b[1;32m 6\u001b[0m df_test \u001b[38;5;241m=\u001b[39m scaler\u001b[38;5;241m.\u001b[39mtransform(df_test)\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'MinMaxScaler' is not defined"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Number of positive: 15953, number of negative: 180854\n",
+ "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.142028 seconds.\n",
+ "You can set `force_row_wise=true` to remove the overhead.\n",
+ "And if memory is not enough, you can set `force_col_wise=true`.\n",
+ "[LightGBM] [Info] Total Bins 10307\n",
+ "[LightGBM] [Info] Number of data points in the train set: 196807, number of used features: 96\n",
+ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
+ "[LightGBM] [Info] Start training from score 0.000000\n",
+ "[CV 4/5] END boosting_type=gbdt, class_weight=balanced, learning_rate=0.05, metric=binary_logloss, n_estimators=10000, num_leaves=31, objective=binary, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8;, score=0.886 total time= 5.6min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Number of positive: 15953, number of negative: 180853\n",
+ "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.138505 seconds.\n",
+ "You can set `force_row_wise=true` to remove the overhead.\n",
+ "And if memory is not enough, you can set `force_col_wise=true`.\n",
+ "[LightGBM] [Info] Total Bins 10304\n",
+ "[LightGBM] [Info] Number of data points in the train set: 196806, number of used features: 96\n",
+ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
+ "[LightGBM] [Info] Start training from score 0.000000\n",
+ "[CV 2/5] END boosting_type=gbdt, class_weight=balanced, learning_rate=0.05, metric=binary_logloss, n_estimators=10000, num_leaves=31, objective=binary, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8;, score=0.883 total time= 7.1min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Number of positive: 15952, number of negative: 180854\n",
+ "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.066573 seconds.\n",
+ "You can set `force_row_wise=true` to remove the overhead.\n",
+ "And if memory is not enough, you can set `force_col_wise=true`.\n",
+ "[LightGBM] [Info] Total Bins 10240\n",
+ "[LightGBM] [Info] Number of data points in the train set: 196806, number of used features: 96\n",
+ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=-0.000000\n",
+ "[LightGBM] [Info] Start training from score -0.000000\n",
+ "[CV 1/5] END boosting_type=gbdt, class_weight=balanced, learning_rate=0.05, metric=binary_logloss, n_estimators=10000, num_leaves=31, objective=binary, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8;, score=0.889 total time= 7.1min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Number of positive: 15953, number of negative: 180853\n",
+ "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.044952 seconds.\n",
+ "You can set `force_row_wise=true` to remove the overhead.\n",
+ "And if memory is not enough, you can set `force_col_wise=true`.\n",
+ "[LightGBM] [Info] Total Bins 10221\n",
+ "[LightGBM] [Info] Number of data points in the train set: 196806, number of used features: 96\n",
+ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
+ "[LightGBM] [Info] Start training from score 0.000000\n",
+ "[CV 3/5] END boosting_type=gbdt, class_weight=balanced, learning_rate=0.05, metric=binary_logloss, n_estimators=10000, num_leaves=31, objective=binary, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8;, score=0.884 total time= 7.1min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[LightGBM] [Info] Number of positive: 15953, number of negative: 180854\n",
+ "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.180802 seconds.\n",
+ "You can set `force_col_wise=true` to remove the overhead.\n",
+ "[LightGBM] [Info] Total Bins 10238\n",
+ "[LightGBM] [Info] Number of data points in the train set: 196807, number of used features: 96\n",
+ "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.500000 -> initscore=0.000000\n",
+ "[LightGBM] [Info] Start training from score 0.000000\n",
+ "[CV 5/5] END boosting_type=gbdt, class_weight=balanced, learning_rate=0.05, metric=binary_logloss, n_estimators=10000, num_leaves=31, objective=binary, reg_alpha=0.1, reg_lambda=0.1, subsample=0.8;, score=0.887 total time= 7.4min\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:97: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n",
+ "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/preprocessing/_label.py:132: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "new_X_train_002 = select_columns(X_train, feature_names, shap_df, 0.002)\n",
+ "new_X_test_002 = select_columns(X_test, feature_names, shap_df, 0.002)\n",
+ "\n",
+ "X_train_002_scaled, X_test_002_scaled = scale_data(new_X_train_002, new_X_test_002 )\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 228,
+ "id": "db117fe4",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
"output_type": "stream",
"text": [
- "A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n"
+ "\n",
+ "---------------------------------\n",
+ "start generate_model_report\n"
]
},
{
- "name": "stderr",
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
"output_type": "stream",
"text": [
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n"
+ "Logistic: f1=0.000 auc=0.202\n"
]
},
{
- "name": "stderr",
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
"output_type": "stream",
"text": [
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n",
- "/Users/markobriesemann/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:1152: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
- " return fit_method(estimator, *args, **kwargs)\n"
+ "+------------------+---------------------+---------------------+\n",
+ "| Confusion Matrix | Positive prediction | Negative prediction |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "| Positive class | True positive (TP) | False negative (FN) |\n",
+ "| Negative class | False positive (FP) | True negative (TN) |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "+------------------+---------------------+---------------------+\n",
+ "| Confusion Matrix | Positive prediction | Negative prediction |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "| Positive class | 0 | 4884 |\n",
+ "| Negative class | 1 | 56618 |\n",
+ "+------------------+---------------------+---------------------+\n",
+ "ROC AUC: 0.4999911690421943\n",
+ "Accuracy = 0.9205729801798286\n",
+ "Precision = 0.0\n",
+ "Recall = 0.0\n",
+ "F1 Score = 0.0\n",
+ "Fbeta Score = (0.49, 0.92, 0.9)\n",
+ " model tn fp fn tp FP+10*FN accuracy ROC_AUC \\\n",
+ "0 RFC_newFEATURE_002 56618 1 4884 0 48841 0.920573 0.499991 \n",
+ "\n",
+ " precision recall F1_Score Fbeta_macro Fbeta_micro Fbeta_weighted \n",
+ "0 0.0 0.0 0.0 0.49 0.92 0.9 \n",
+ "---------------------------------\n",
+ "start find_optimal_business_score\n",
+ "prediction proba 61503\n",
+ "Y_true 61503\n",
+ "Series([], Name: best, dtype: object)\n",
+ "0 1\n",
+ "Name: best, dtype: object\n",
+ "best b score 35370 1 0.1\n",
+ "Name: threshold, dtype: float64\n",
+ " threshold tn fp fn tp FP+10*FN accuracy ROC_AUC \\\n",
+ "0 0.0 0 56619 0 4884 56619 0.079411 0.500000 \n",
+ "1 0.1 39599 17020 1835 3049 35370 0.693430 0.661839 \n",
+ "2 0.2 53621 2998 3793 1091 40928 0.889583 0.585216 \n",
+ "3 0.3 56188 431 4602 282 46451 0.918167 0.525064 \n",
+ "4 0.4 56578 41 4843 41 48471 0.920589 0.503835 \n",
+ "5 0.5 56618 1 4884 0 48841 0.920573 0.499991 \n",
+ "\n",
+ " precision recall F1_Score Fbeta_macro Fbeta_micro Fbeta_weighted \\\n",
+ "0 0.079411 1.000000 0.147137 0.150668 0.079411 0.023929 \n",
+ "1 0.151926 0.624283 0.244379 0.561981 0.693430 0.710915 \n",
+ "2 0.266813 0.223382 0.243174 0.587648 0.889583 0.887738 \n",
+ "3 0.395512 0.057740 0.100768 0.523806 0.918167 0.905846 \n",
+ "4 0.500000 0.008395 0.016512 0.496529 0.920589 0.905408 \n",
+ "5 0.000000 0.000000 0.000000 0.491513 0.920573 0.904964 \n",
+ "\n",
+ " best \n",
+ "0 0 \n",
+ "1 1 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 \n",
+ "5 0 \n",
+ "Artifact PATH RFC_newFEATURE_002_artifactPATH\n",
+ "{'TN': 39599, 'FP': 17020, 'FN': 1835, 'TP': 3049, 'FP_10_FN': 35370, 'Accuracy': 0.6934295888005463, 'F1': 0.2443794333346692, 'Precision': 0.15192585579749862, 'Recall': 0.6242833742833743, 'ROC_AUC': 0.6618387852889521, 'threshold': 0.1, 'time_in_s': 5635.813629388809}\n",
+ "{'max_depth': None, 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 100}\n",
+ "Active run_id: 099cbabe3cbf4842946dcd09a6e7710e\n"
+ ]
+ }
+ ],
+ "source": [
+ "run_name = \"RFC_newFEATURE_002\"\n",
+ "RFC_model_002, best_RFC_params, time_RFC = RFC_model(new_X_train_002, Y_train)\n",
+ "RFC_metrics, best_metrics_RFC = generate_model_report(RFC_model_002, run_name, new_X_test_002, Y_test, time_RFC)\n",
+ "run_MLflow(experiment_name, run_name, RFC_metrics, \n",
+ " best_RFC_params, RFC_model_002, new_X_train_002)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 231,
+ "id": "339217b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "START time Sat Mar 2 16:58:13 2024\n",
+ "start RandomizedSearchCV \n",
+ "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The total space of parameters 6 is smaller than n_iter=100. Running 6 iterations. For exhaustive searches, use GridSearchCV.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Best Hyperparameters: {'max_depth': None, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
- "START time Fri Mar 1 11:46:14 2024\n",
- "END time Fri Mar 1 15:10:30 2024 duration 204.27139929930368 min\n",
+ "START time Sat Mar 2 16:58:13 2024\n",
+ "END time Sat Mar 2 17:04:55 2024 duration 6.689607028166453 min\n",
"\n",
"---------------------------------\n",
"start generate_model_report\n"
@@ -3887,7 +4995,7 @@
},
{
"data": {
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",
+ "image/png": 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",
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