Scikit-Learn GridSearchCV QuantileRegressor

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 QuantileRegressor, a robust algorithm for quantile regression tasks.

Grid search is a method for evaluating 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.

QuantileRegressor is used for quantile regression, which predicts a specified quantile of the target distribution, rather than the mean. This makes it robust to outliers in the target variable.

The key hyperparameters for QuantileRegressor include the regularization strength (alpha), which helps control overfitting, and the quantile (quantile), which specifies the quantile to be predicted.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import QuantileRegressor

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, noise=0.1, random_state=42)

# 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],
    'quantile': [0.1, 0.5, 0.9]
}

# Perform grid search
grid_search = GridSearchCV(estimator=QuantileRegressor(),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_absolute_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_
score = best_model.score(X_test, y_test)
print(f"Test set score: {score:.3f}")

Running the example gives an output like:

Best score: -0.132
Best parameters: {'alpha': 0.1, 'quantile': 0.5}
Test set score: 1.000

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for alpha and quantile hyperparameters.
4. Perform grid search using GridSearchCV, specifying the QuantileRegressor model, parameter grid, 5-fold cross-validation, and negative mean absolute 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 can easily explore different hyperparameter settings and find the combination that maximizes the model’s performance. This automated approach saves time and effort compared to manual hyperparameter tuning and helps ensure we select the best configuration for our QuantileRegressor model.