Overfitting, Cross-Validation and Model Tuning

In this lesson, we will learn overfitting, underfitting, cross-validation, and model tuning. Train/test difference, bias-variance trade-off, hyperparameters, validation data, grid search, random search, and proper model performance improvement will be explained. ## Lesson objective In this lesson, we will learn Overfitting, Cross-Validation and Model Tuning. In the previous lesson, we covered Feature Engineering and Data Transformation. We explained missing value handling, encoding, scaling, standardization, normalization, binning, date features, RFM, lag feature, rolling feature, interaction feature, text feature, log transformation, outlier handling, and data leakage.