query: Is cross-validation enough to prevent overfitting?
positive_passages.docid: 150758
positive_passages.text: How does cross-validation overcome the overfitting problem?
negative_passages.docid: 108205
negative_passages.text: Should I use a categorical cross-entropy or binary cross-entropy loss for binary predictions?
negative_passages.docid: 113807
negative_passages.text: Testing Classification on Oversampled Imbalance Data
negative_passages.docid: 79352
negative_passages.text: Definition of Overfitting
negative_passages.docid: 126595
negative_passages.text: Does feature selection help improve the performance of machine learning?
negative_passages.docid: 58047
negative_passages.text: Decision Trees and Regression - Can predicted values be outside range of training data?
negative_passages.docid: 37025
negative_passages.text: Choice of K in K-fold cross-validation
negative_passages.docid: 145878
negative_passages.text: Overfitting on the loss graph, but not the accuracy graph
negative_passages.docid: 25409
negative_passages.text: Why does k-fold cross validation generate an MSE estimator that has higher bias, but lower variance then leave-one-out cross-validation?
negative_passages.docid: 121682
negative_passages.text: Standardize Training and Validation Data
negative_passages.docid: 76216
negative_passages.text: Should I use multi-label classification?
negative_passages.docid: 130691
negative_passages.text: Compare classifiers based on AUROC or accuracy?
negative_passages.docid: 54879
negative_passages.text: Using x to predict y when they are highly correlated
negative_passages.docid: 118276
negative_passages.text: Multivariate multiple regression validation
negative_passages.docid: 156475
negative_passages.text: Validation Error less than training error?
negative_passages.docid: 118753
negative_passages.text: Is feature selection with dummy coding of categorical variables problematic?
negative_passages.docid: 17831
negative_passages.text: Training on the full dataset after cross-validation?