How can I implement hyperparameter tuning in Python?

How can I implement hyperparameter tuning in Python?

You can use libraries like Scikit-learn or Optuna for hyperparameter tuning. Would you like a code example?

Yes, please provide a code example using Scikit-learn.

Sure! Here's a simple example using GridSearchCV:

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

# Define the model
model = RandomForestClassifier()

# Define the parameter grid
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [None, 10, 20, 30],
}

# Set up GridSearchCV
grid_search = GridSearchCV(model, param_grid, cv=5)

# Fit the model
grid_search.fit(X_train, y_train)

# Best parameters
print(grid_search.best_params_)

This will help you find the best parameters for your model!

What are some other methods for hyperparameter tuning?

Other methods include Random Search, Bayesian Optimization, and using libraries like Hyperopt or Optuna for more advanced tuning.

Can you explain Bayesian Optimization?

Bayesian Optimization is a probabilistic model-based approach to optimize hyperparameters. It builds a surrogate model to predict the performance of different hyperparameter combinations and chooses the next set of parameters to evaluate based on this model.