This repository contains GIF animations of classifiers learning a decision rule. We can visualize the rule a classifier learns as a decision boundary separating the features of each class. In the animations, the optimal decision boundary is drawn with a dashed line. In a sense, the "goal" of a classifier is to fit a curve to this optimal boundary.
The animations depict a binary classification problem over two distributions. A mvn in the filename indicates the features of each class are distributed normally; a mix in the filename indicates the features of each class are distributed as a mixture of normal distributions.
The animations were created in R, using the {gganimate} package. A repository of functions to generate the animations can be found here.
The classifiers illustrated are:
- Extreme Learning Machine with
{elmNNRcpp::elm_train} - Gaussian Process with
{kernlab::gausspr} - Generalized Additive Model with
{mgcv::gam} - Generalized Linear Model with
{stats::glm} - k-Nearest Neighbors with
{class::knn} - Multivariate Adaptive Regression Spline with
{earth::earth} - Mixture Discriminant Analysis with
{mda::mda} - Naive Bayes Classifier with
{naivebayes::naive_bayes} - Feedforward Neural Network with
{nnet::nnet} - Polynomial MARS with
{polspline::polymars} - Boosted P-Splines with
{mboost::mboost} - Quadratic Discriminant Analysis with
{MASS::qda} - Random Forest with
{ranger::ranger} - Decision Tree with
{rpart::rpart} - Support Vector Machine using RBF kernel with
{kernlab::ksvm} - Gradient Boosting with
{xgboost::xgboost}
- Thomas Lin Pedersen and David Robinson (2019). gganimate: A Grammar of Animated Graphics. R package version 1.0.4. https://CRAN.R-project.org/package=gganimate
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- Lampros Mouselimis and Alberto Gosso (2018). elmNNRcpp: The Extreme Learning Machine Algorithm. R package version 1.0.1. https://CRAN.R-project.org/package=elmNNRcpp
- R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Wood, S.N. (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC.
- Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0
- Stephen Milborrow. Derived from mda:mars by Trevor Hastie and Rob Tibshirani. Uses Alan Miller's Fortran utilities with Thomas Lumley's leaps wrapper. (2019). earth: Multivariate Adaptive Regression Splines. R package version 5.1.2. https://CRAN.R-project.org/package=earth
- Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0
- Charles Kooperberg (2019). polspline: Polynomial Spline Routines. R package version 1.1.17. https://CRAN.R-project.org/package=polspline
- Marvin N. Wright, Andreas Ziegler (2017). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77(1), 1-17. doi:10.18637/jss.v077.i01
- Terry Therneau and Beth Atkinson (2019). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-15. https://CRAN.R-project.org/package=rpart
- Alexandros Karatzoglou, Alex Smola, Kurt Hornik, Achim Zeileis (2004). kernlab - An S4 Package for Kernel Methods in R. Journal of Statistical Software 11(9), 1-20. URL http://www.jstatsoft.org/v11/i09/
- Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang, Hyunsu Cho, Kailong Chen, Rory Mitchell, Ignacio Cano, Tianyi Zhou, Mu Li, Junyuan Xie, Min Lin, Yifeng Geng and Yutian Li (2019). xgboost: Extreme Gradient Boosting. R package version 0.90.0.2. https://CRAN.R-project.org/package=xgboost
Decision Boundaries Animations by Ryan Holbrook is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at https://github.com/ryanholbrook/decision-boundaries-animations.
