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

Language 2023-06-04 22:44:51 views: null

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, custom tuning evaluation indicators, tuneLength parameters and tuneGrid parameters hyperparameter tuning

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Origin blog.csdn.net/zhongkeyuanchongqing/article/details/130916153

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 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 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 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 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 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 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 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 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 multi-layer perceptron algorithm model (MLP) to build a classification model, the trainControl function to set cross-validation parameters, and customize tuning evaluation indicators

Naive Bayesian and Probabilistic Model 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.

Evaluation classification model and model selection and tuning

Naive Bayesian model training (wdbc data set)

Image classification - use pytorch to build an AlexNet network model to train your own data set (cat and dog classification)

Model Evaluation and loss of function

[Bayesian model] BO-KNN Bayesian optimized nearest neighbor classification prediction (including K-fold cross-validation accuracy)

Evaluate the classification model—Confusion Matrix and evaluation indicators

[Bayesian model] Realize Naive Bayesian (Naive Bayesian) multi-feature classification prediction (without calling the toolbox)

CLASSIFICATION language model R

Naive Bayesian machine learning model (text classification, spam recognition)

Notes - Evaluation Model: K-fold cross-validation

R language to build RFM model to understand~~~

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