R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators - Code World

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

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R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a penalty discriminant analysis model (pda) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a naive Bayesian model (NB) model to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a KNN proximity algorithm model (KNN) model to build a classification model, trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build an xgboost model (based on the gbtree algorithm) model to build a classification model, trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build an xgboost model (based on the gbtree algorithm) model to build a classification model, trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a conditional inference tree model algorithm (ctree) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a regularized generalized linear model (glmnet) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a regularized generalized linear model (glmnet) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a Bagging AdaBoost model (Bagged AdaBoost) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a Bagging AdaBoost model (Bagged AdaBoost) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a multi-layer perceptron algorithm model (MLP) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the train function of the caret package to build a multi-layer perceptron algorithm model (MLP) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

R language uses the lift_curve function of the yardstick package to evaluate the performance of the multiclass (Multiclass) model and view the lift curve (lift curve) of the model on each classification of the multiclass.

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