Скачать или смотреть Overview of GridSearchCV in Scikit-learn

GridSearchCV is a systematic approach to hyperparameter tuning in Scikit-learn. It performs an exhaustive search over specified parameter values for an estimator. The process involves defining a grid of hyperparameters and evaluating the model performance for each combination using cross-validation. This ensures that the best set of hyperparameters is selected based on the validation set performance, not just the training data. Scikit-learn’s implementation of GridSearchCV makes this process convenient and straightforward. You define the parameter grid and the number of folds for cross-validation, and GridSearchCV handles the rest, fitting the model multiple times and scoring it against the validation data.