Lasso model selection: Cross-Validation / AIC / BIC - Code World

Lasso model selection: Cross-Validation / AIC / BIC

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Origin blog.csdn.net/qq_42946328/article/details/111592023

Lasso model selection: Cross-Validation / AIC / BIC

Dry | (with examples) BIC model selection criteria of Detailed article and AIC

aic bic mdl

Model cross-validation concept and code summary

Using cross-validation (Cross Validation) for model assessment

"Statistical Learning Methods" (Li Hang): model evaluation selection, regularization and cross-validation, generalization ability, generative model and discriminative model, supervised learning application

最优模型选择的准则：AIC、BIC准则

Notes - Evaluation Model: K-fold cross-validation

Model improvement and generalization (bias variance and cross-validation)

Tool series: TimeGPT_(9) model cross-validation

Sélection du modèle de lasso: validation croisée / AIC / BIC

Seleção do modelo Lasso: Validação Cruzada / AIC / BIC

Auswahl des Lasso-Modells: Kreuzvalidierung / AIC / BIC

Выбор модели лассо: перекрестная проверка / AIC / BIC

Li Hongyi machine learning assignment 3 - Image Classification, model integration, cross-validation, TTA

Machine Learning Fundamentals: Feature Selection with Lasso

[Bioinformatics] Feature selection using HSIC LASSO method

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

Cross-validation points

Cross-validation ideas

Keras model for cross validation

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 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 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 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

Linear Model Optimization: Ridge Regression and Lasso Regression

Use the AIC function to evaluate the AIC value of a conditional logistic regression model (R language)

Cross-validation (Cross Validation) principle Summary

What is cross-validation (Cross Validation)?

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