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# Tokyo r11caret

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### Tokyo r11caret

1. 1. 1.2 Proposed ProcedureMany beginners use the following procedure now: • Transform data to the format of an SVM package • Randomly try a few kernels and parameters • TestWe propose that beginners try the following procedure ﬁrst: • Transform data to the format of an SVM package • Conduct simple scaling on the data 2 • Consider the RBF kernel K(x, y) = e−γx−y • Use cross-validation to ﬁnd the best parameter C and γ • Use the best parameter C and γ to train the whole training set5 • TestWe discuss this procedure in detail in the following sections. A Practical Guide to Support Vector Classification2 Data Preprocessing
2. 2. 1.2 Proposed ProcedureMany beginners use the following procedure now: • Transform data to the format of an SVM package • Randomly try a few kernels and parameters • TestWe propose that beginners try the following procedure ﬁrst: • Transform data to the format of an SVM package • Conduct simple scaling on the data 2 • Consider the RBF kernel K(x, y) = e−γx−y • Use cross-validation to ﬁnd the best parameter C and γ • Use the best parameter C and γ to train the whole training set5 • TestWe discuss this procedure in detail in the following sections. A Practical Guide to Support Vector Classification2 Data Preprocessing
3. 3. Table 1: Models used in train Model method Value Package Tuning Parameters “Dual–Use Models” Generalized linear model glm stats None glmStepAIC MASS None Generalized additive model gam mgcv select, method gamLoess gam span, degree gamSpline gam df Recursive Partitioning rpart rpart maxdepth ctree party mincriterion ctree2 party maxdepth Boosted Trees gbm gbm n.trees, shrinkage interaction.depth blackboost mboost maxdepth, mstop ada ada maxdepth, iter, nu Other Boosted Models glmboost mboost mstop10 gamboost mboost mstop Random Forests rf randomForest mtry parRF randomForest, foreach mtry cforest party mtry Boruta Boruta mtry Bagging treebag ipred None bag caret vars logicBag logicFS ntrees, nleaves Other Trees nodeHarvest nodeHarvest maxinter, mode partDSA partDSA cut.off.growth, MPD Multivariate Adaptive Regression Splines earth, mars earth degree, nprune gcvEarth earth degree Bagged MARS bagEarth caret, earth degree, nprune Logic Regression logreg LogicReg ntrees, treesize Elastic Net (glm) glmnet glmnet alpha, lambda (continued on next page)
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