Scikit-Learn GridSearchCV MLPClassifier

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 MLPClassifier, a popular neural network algorithm for classification 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.

MLPClassifier (Multi-layer Perceptron) is a type of feedforward artificial neural network used for classification. It consists of multiple layers of neurons with non-linear activation functions that allow it to model complex patterns in the data.

The key hyperparameters for MLPClassifier include hidden_layer_sizes, which determines the size of the hidden layers; activation, which specifies the activation function used by the neurons; solver, which is the optimization algorithm used to find the model weights; and alpha, which is the L2 regularization term that helps prevent overfitting.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neural_network import MLPClassifier

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10, n_redundant=10, 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 = {
    'hidden_layer_sizes': [(50,), (100,), (50,50)],
    'activation': ['tanh', 'relu'],
    'solver': ['sgd', 'adam'],
    'alpha': [0.0001, 0.001, 0.01]
}

# Perform grid search
grid_search = GridSearchCV(estimator=MLPClassifier(max_iter=200, random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy')
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_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.942
Best parameters: {'activation': 'relu', 'alpha': 0.0001, 'hidden_layer_sizes': (100,), 'solver': 'adam'}
Test set accuracy: 0.950

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for hidden_layer_sizes, activation, solver, and alpha hyperparameters.
4. Perform grid search using GridSearchCV, specifying the MLPClassifier model, parameter grid, 5-fold cross-validation, and accuracy 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 accuracy.

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 MLPClassifier model.