Configure SGDClassifier "penalty" Parameter

The penalty parameter in scikit-learn’s SGDClassifier determines the type of regularization applied to the model during training.

Stochastic Gradient Descent (SGD) is an efficient method for training linear classifiers, particularly useful for large-scale learning. The penalty parameter controls the regularization term, which helps prevent overfitting.

The penalty parameter affects the model’s ability to generalize by adding a penalty term to the loss function, discouraging complex models. Different penalties lead to different types of regularization.

The default value for penalty is ’l2’. Common options include ’l2’ (Ridge), ’l1’ (Lasso), and ’elasticnet’ (combination of L1 and L2).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import SGDClassifier
from sklearn.metrics import accuracy_score
import numpy as np

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=10,
                           n_redundant=5, 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)

# Train with different penalty options
penalties = ['l2', 'l1', 'elasticnet']
accuracies = []

for penalty in penalties:
    if penalty == 'elasticnet':
        sgd = SGDClassifier(penalty=penalty, l1_ratio=0.5, random_state=42)
    else:
        sgd = SGDClassifier(penalty=penalty, random_state=42)

    sgd.fit(X_train, y_train)
    y_pred = sgd.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    print(f"Penalty: {penalty}, Accuracy: {accuracy:.3f}")

# Compare feature importance (absolute values of coefficients)
for penalty, sgd in zip(penalties, [SGDClassifier(penalty=p, random_state=42).fit(X_train, y_train) for p in penalties]):
    coef_abs = np.abs(sgd.coef_[0])
    print(f"\nPenalty: {penalty}")
    print(f"Number of non-zero features: {np.sum(coef_abs > 1e-5)}")
    print(f"Top 5 feature importances: {coef_abs.argsort()[-5:][::-1]}")

Running the example gives an output like:

Penalty: l2, Accuracy: 0.770
Penalty: l1, Accuracy: 0.775
Penalty: elasticnet, Accuracy: 0.775

Penalty: l2
Number of non-zero features: 20
Top 5 feature importances: [11  2 17 14 18]

Penalty: l1
Number of non-zero features: 10
Top 5 feature importances: [11 14 17  2 15]

Penalty: elasticnet
Number of non-zero features: 14
Top 5 feature importances: [11 14 15  3  2]

The key steps in this example are:

Generate a synthetic binary classification dataset with informative and noise features
Split the data into train and test sets
Train SGDClassifier models with different penalty values
Evaluate the accuracy of each model on the test set
Compare the number of non-zero features and top feature importances for each penalty

Some tips and heuristics for setting the penalty parameter:

Use ’l2’ (default) for a good balance between model complexity and generalization
Consider ’l1’ when you suspect many features are irrelevant (promotes sparsity)
Try ’elasticnet’ to combine the benefits of both L1 and L2 regularization
Experiment with different penalties and compare model performance

Issues to consider:

The choice of penalty depends on the nature of your data and the problem at hand
’l1’ penalty may lead to sparse solutions, which can be beneficial for feature selection
’elasticnet’ requires tuning an additional parameter (l1_ratio) to balance L1 and L2
The effect of the penalty may vary depending on the scale of your features

See Also