Scikit-Learn GridSearchCV MultiTaskLasso

Hyperparameter tuning is a crucial step in optimizing machine learning models for best performance. In this example, we’ll demonstrate how to use scikit-learn’s GridSearchCV to perform hyperparameter tuning for MultiTaskLasso, a model used for multitask regression problems.

Grid search evaluates different combinations of model hyperparameters to find the best performing configuration. It exhaustively searches through a specified parameter grid, trains and evaluates the model for each combination using cross-validation, and selects the hyperparameters that yield the best performance metric.

MultiTaskLasso is a linear model designed for multitask learning, where multiple regression tasks are solved together with a shared representation. The key hyperparameters include alpha, which controls the regularization strength, and max_iter, which sets the maximum number of iterations for the solver.

from sklearn.datasets import make_multilabel_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import MultiTaskLasso

# Generate synthetic dataset for multitask regression
X, y = make_multilabel_classification(n_samples=1000, n_features=20, n_classes=3, random_state=42)
y = y.astype(float)  # Convert to float to simulate regression targets

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define parameter grid
param_grid = {
    'alpha': [0.1, 1.0, 10.0],
    'max_iter': [1000, 2000, 3000]
}

# Perform grid search
grid_search = GridSearchCV(estimator=MultiTaskLasso(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {grid_search.best_score_:.3f}")
print(f"Best parameters: {grid_search.best_params_}")

# Evaluate on test set
best_model = grid_search.best_estimator_
test_score = best_model.score(X_test, y_test)
print(f"Test set score: {test_score:.3f}")

Running the example gives an output like:

Best score: -0.146
Best parameters: {'alpha': 0.1, 'max_iter': 1000}
Test set score: 0.382

The steps are as follows:

1. Generate a synthetic multitask regression dataset using scikit-learn’s make_multilabel_classification function and convert the targets to float.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for alpha and max_iter hyperparameters.
4. Perform grid search using GridSearchCV, specifying the MultiTaskLasso model, parameter grid, 5-fold cross-validation, and negative mean squared error scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by grid search.
6. Evaluate the best model on the hold-out test set and report the score.

By using GridSearchCV, we efficiently explore different hyperparameter settings to find the best configuration for the MultiTaskLasso model, which improves its performance on multitask regression problems. This approach automates hyperparameter tuning, saving time and effort compared to manual tuning.