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FilenameDemos featured inDescription/Notes
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ml_binaryclass_baggingdemo_binaryclass_baggingBinary classification based on the highest prediction among models to bootstrap samples.
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ml_binaryclass_basisdemo_binaryclass_basis, demo_binaryclass_binaryclass_CVBinary classification after a change of basis
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ml_binaryclass_boostingdemo_binaryclass_boostingImplements AdaBoost or LogitBoost algorithm to fit a binary classifier
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ml_binaryclass_brokenStumpdemo_binaryclass_exponentialFinds the best threshold across all features
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ml_binaryclass_Cauchitdemo_binaryclass_GLMClassifies data using generalized linear model with a Cauchit link
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ml_binaryclass_CVdemo_binaryclass_CVThis computes the "best" hyper-parameter(s) using cross-validation for classification and regression problems
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ml_binaryclass_exponentialdemo_binaryclass_exponentialFits a linear classifier by minimizing the exponential loss
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ml_binaryclass_extremedemo_binaryclass_GLMClassifies data using generalized linear model with a extreme link
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ml_binaryclass_HSVMdemo_binaryclass_SVMFits a linear classifier using Huberized SVM
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ml_binaryclass_logisticdemo_binaryclass_bagging, demo_binaryclass_basis, demo_binaryclass_binaryclass_CV, demo_binaryclass_GLMFits a classifier using logistic regression with logistic output layer and logistic loss
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ml_binaryclass_MLPdemo_binaryclass_NNBinary classification using a multilayer perceptron with logistic loss
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ml_binaryclass_multiclassdemo_binaryclass_altUsing a multiclass classifier and treating the special case of binary labels
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ml_binaryclass_perceptrondemo_binaryclass_NNFits a binary linear classifier by using the perceptron learning algorithm
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ml_binaryclass_probitdemo_binaryclass_GLMFits a probit regression classifier by minimizing the negative likelihood
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ml_binaryclass_randomForestdemo_binaryclass_decisionsA bagging of tree classifers, where the splits are chosen among random selection of features
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ml_binaryclass_regressiondemo_binaryclass_altClassification by using a regression model
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ml_binaryclass_SSVMdemo_binaryclass_SVMFits a smooth support vector machine by minimizing squared hinge loss

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ml_binaryclass_stumpdemo_binaryclass_bagging, demo_binaryclass_boosting, demo_binaryclass_decisionsFinds the best threshold across all features
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ml_binaryclass_SVMdemo_binaryclass_SVMFits a linear classifier by maximizing the margin using SVM
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ml_binaryclass_treedemo_binaryclass_decisionsBinary classification using a decision tree. The tree is constructed using the C4.5 algorithm, based on maximizing information gain