Data Science
Hyperparameter Tuning with Grid Search
Senior Data Analyst
May 14, 2026
5 min read
Grid Search
from sklearn.model_selection import GridSearchCV
params = {'max_depth': [3, 5, 10], 'n_estimators': [50, 100, 200]}
grid = GridSearchCV(RandomForestClassifier(), params, cv=5)
grid.fit(X_train, y_train)
print(f"Best Params: {grid.best_params_}")
print(f"Best Score: {grid.best_score_:.2%}")
*Day 135: Handling Imbalanced Data.*