query: Why is cross validation better than training once with the whole data set?
positive_passages.docid: 54302
positive_passages.text: Cross-Validation in plain english?
negative_passages.docid: 143081
negative_passages.text: How to choose the right number of CV folds for the nested cross validation for a small sample?
negative_passages.docid: 57060
negative_passages.text: Is normalizing the features always good for classification?
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: 82605
negative_passages.text: Why are the number of false positives independent of sample size, if we use p-values to compare two independent datasets?
negative_passages.docid: 26160
negative_passages.text: One-vs-All and One-vs-One in svm?
negative_passages.docid: 148960
negative_passages.text: Out of Bag Error makes CV unnecessary in Random Forests?
negative_passages.docid: 18820
negative_passages.text: For imbalanced classification, should the validation dataset be balanced?
negative_passages.docid: 70819
negative_passages.text: Why do Random forest and XGBoost gives different importance weight on the same set of features?
negative_passages.docid: 129280
negative_passages.text: Neural networks: why do we randomize the training set?
negative_passages.docid: 102544
negative_passages.text: Why should one use EM vs. say, Gradient Descent with MLE?
negative_passages.docid: 124109
negative_passages.text: K-fold vs. Monte Carlo cross-validation
negative_passages.docid: 37141
negative_passages.text: Cross-validation for timeseries data with regression
negative_passages.docid: 104621
negative_passages.text: On the hardness of data to learn
negative_passages.docid: 73962
negative_passages.text: Is it always better to use the whole dataset to train the final model?
negative_passages.docid: 78589
negative_passages.text: Why mini batch size is better than one single "batch" with all training data?
negative_passages.docid: 59364
negative_passages.text: Why should the data be shuffled for machine learning tasks