How can I tune hyperparameters in scikit-learn?

How can I tune hyperparameters in scikit-learn?

You can use techniques like Grid Search and Random Search to tune hyperparameters in scikit-learn.

What is Grid Search and how do I implement it?

Grid Search is a method to exhaustively search through a specified subset of hyperparameters. You can implement it using GridSearchCV from sklearn.model_selection.

Can you show me a code example for Grid Search?

Sure! Here's a simple example:

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

param_grid = {'n_estimators': [10, 50, 100], 'max_depth': [None, 10, 20]}
model = RandomForestClassifier()
grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X_train, y_train)
print(grid_search.best_params_)

What about Random Search? Is it different?

Yes, Random Search samples a fixed number of parameter settings from specified distributions. It's often faster than Grid Search.