Used GridSeachCv to search through the Hyperparameter space
rf_regressor = RandomForestRegressor(random_state=42)
grid_search = GridSearchCV(estimator=rf_regressor, param_grid=param_grid, cv=5, scoring='neg_mean_absolute_error')
grid_search.fit(X_train, y_train)
best_params = grid_search.best_params_
# Model Training with best hyperparameters
rf_regressor = RandomForestRegressor(**best_params, random_state=42)
rf_regressor.fit(X_train, y_train)Given a parameter grid of Hyperparameter, a model, then you model it on the hypers, then gives you the best hypers, that gives the highest cross validation performance.
