query: In Random Forest, cross-validation can be avoided using the out-of-bag sample?
positive_passages.docid: 148960
positive_passages.text: Out of Bag Error makes CV unnecessary in Random Forests?
negative_passages.docid: 36335
negative_passages.text: When wouldn't I use LASSO for model selection?
negative_passages.docid: 114625
negative_passages.text: Model selection and cross-validation: The right way
negative_passages.docid: 64175
negative_passages.text: How to estimate confidence level for SVM or Random Forest?
negative_passages.docid: 144805
negative_passages.text: Bag-of-Words for Text Classification: Why not just use word frequencies instead of TFIDF?
negative_passages.docid: 48283
negative_passages.text: Feature selection and cross-validation
negative_passages.docid: 19145
negative_passages.text: Validation of machine learning algorithms implementation
negative_passages.docid: 81645
negative_passages.text: Combining PCA, feature scaling, and cross-validation without training-test data leakage
negative_passages.docid: 124109
negative_passages.text: K-fold vs. Monte Carlo cross-validation
negative_passages.docid: 13076
negative_passages.text: Is random forest a boosting algorithm?
negative_passages.docid: 37025
negative_passages.text: Choice of K in K-fold cross-validation
negative_passages.docid: 101873
negative_passages.text: Leave one out cross validation for LSTM
negative_passages.docid: 2883
negative_passages.text: Can Tree-based regression perform worse than plain linear regression?
negative_passages.docid: 32234
negative_passages.text: Reference for random forests
negative_passages.docid: 34923
negative_passages.text: How does leave-one-out cross-validation work? How to select the final model out of $n$ different models?
negative_passages.docid: 104438
negative_passages.text: Random Forest mtry Spit-to-Split Variable Sampling Procedure
negative_passages.docid: 29443
negative_passages.text: Cross Validation for mixed models?