Content:
– Evaluating model performance;
– Verifying the ability to generalize:
– Split validation;
– Stratification;
– The validation set and model selection;
– Cross-validation;
– Performance measures:
– For classification:
– Why accuracy is not enough;
– ROC analysis etc.;
– Micro/macro averaging for multi-class problems;
– For regression;
– Bias vs. variance trade-off;
– Regularization methods;
