How to pass elegantly Sklearn’s GridseachCV’s best parameters to another model?

Passing GridSearchCV Best Parameters to Another Model

GridSearchCV is a powerful tool in scikit-learn for finding the optimal hyperparameters for a machine learning model. However, you might want to use those best parameters with a different model after your GridSearchCV search is finished. Here’s how to do it elegantly.

Understanding GridSearchCV

GridSearchCV systematically explores a range of hyperparameter combinations, trains a model for each combination, and selects the parameters that result in the best performance based on a chosen metric.

Extracting the Best Parameters

The best parameters found by GridSearchCV are stored in the best_params_ attribute of the fitted GridSearchCV object.

Applying the Best Parameters to Another Model

Once you have the best parameters, you can simply pass them as arguments to the constructor of your desired model.

Example:

Let’s assume you have performed GridSearchCV for a RandomForestClassifier and want to use the best parameters with a LogisticRegression model.

Code

from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris

# Load data
iris = load_iris()
X = iris.data
y = iris.target

# Define hyperparameter grid for RandomForestClassifier
param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [5, 10, 20]
}

# Create and fit GridSearchCV object
rfc = RandomForestClassifier()
grid_search = GridSearchCV(rfc, param_grid, cv=5)
grid_search.fit(X, y)

# Get best parameters
best_params = grid_search.best_params_

# Create LogisticRegression model with best parameters
logistic_model = LogisticRegression(**best_params)
logistic_model.fit(X, y)

Explanation

• We first define a hyperparameter grid for the RandomForestClassifier. This is the grid that GridSearchCV will explore.
• Next, we create a GridSearchCV object, specifying the model, hyperparameter grid, and number of cross-validation folds. We fit this object to our data.
• We retrieve the best parameters found by GridSearchCV using the best_params_ attribute.
• Finally, we create a new LogisticRegression model and pass the best_params dictionary to its constructor. This ensures the LogisticRegression model is initialized with the best hyperparameters determined by GridSearchCV.

Conclusion

By using the best_params_ attribute of GridSearchCV, you can easily pass the optimal hyperparameters to another model. This provides a convenient way to leverage the benefits of hyperparameter optimization across different models.