Quadratic discriminant analysis {MASS::qda} with normal features. The QDA model is the same as the data model in this case, and so it fits the optimal boundary very closely. (2/n)pic.twitter.com/ZYRkwIXaMV
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Decision tree {rpart::rpart} with features distributed as a mixture of normal distributions. (5/n)pic.twitter.com/466jK96g82
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Random forest {ranger::ranger} on mixture of normals. (6/n)pic.twitter.com/WdZIiRvhSX
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GAM {mgcv::gam} with spline smoother on mixture of normals. (8/n)pic.twitter.com/2JYUlWL6FA
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SVM {kernlab::ksvm} with RBF kernel on mixture of normals. (9/n)pic.twitter.com/ogf7YFRg93
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Also, a blog post on optimal decision boundaries: https://mathformachines.com/posts/decision/ It has most of the plotting code. I'm planning on writing another on classifiers with the animation code. In the meantime, here's a gist: https://gist.github.com/ryanholbrook/220f39a669a870f4d312e46b67302106 …pic.twitter.com/xi69xkhZmQ
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Here is a repo with the gif files. I added an open license on the page, but basically you can use them however you like with attribution.https://github.com/ryanholbrook/decision-boundaries-animations …
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Gaussian process {kernlab::gausspr} on mixture of normals. (11/n)pic.twitter.com/b4XC63xG4c
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Extreme learning machine {elmNNRcpp::elm_train} on mixture of normals. (12/n)pic.twitter.com/Z3RrCMucCf
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Mixture discriminant analysis {mda::mda} on mixture of normals. (13/n)pic.twitter.com/qOhNprb1Rx
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Naive Bayes {naivebayes::naive_bayes} on normal features. (14/n)pic.twitter.com/53fhtenxrU
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Boosted p-splines {mboost::mboost} on mixture of normals. (15/n)pic.twitter.com/oMcln4ZZ1N
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