Configure SGDClassifier "alpha" Parameter

The alpha parameter in scikit-learn’s SGDClassifier controls the regularization strength, which helps prevent overfitting.

SGDClassifier uses stochastic gradient descent for optimization, making it efficient for large-scale learning. The alpha parameter determines the weight of the regularization term in the loss function.

Higher alpha values increase regularization, potentially reducing overfitting but risking underfitting. Lower values decrease regularization, potentially allowing for more complex models but risking overfitting.

The default value for alpha is 0.0001. In practice, values are often tuned in the range of 1e-5 to 1.0, depending on the dataset and problem complexity.

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
import matplotlib.pyplot as plt

# 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 alpha values
alpha_values = [1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1.0]
accuracies = []

for alpha in alpha_values:
    sgd = SGDClassifier(alpha=alpha, random_state=42, max_iter=1000, tol=1e-3)
    sgd.fit(X_train, y_train)
    y_pred = sgd.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    print(f"alpha={alpha:.5f}, Accuracy: {accuracy:.3f}")

Running the example gives an output like:

alpha=0.00001, Accuracy: 0.765
alpha=0.00010, Accuracy: 0.770
alpha=0.00100, Accuracy: 0.760
alpha=0.01000, Accuracy: 0.785
alpha=0.10000, Accuracy: 0.805
alpha=1.00000, Accuracy: 0.795

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 alpha values
Evaluate the accuracy of each model on the test set

Some tips for setting the alpha parameter:

Start with the default value and adjust based on model performance
Use logarithmic scale when searching for optimal alpha values
Consider using cross-validation to find the best alpha for your dataset
Higher alpha values work well for simpler datasets or when you want to prevent overfitting
Lower alpha values may be suitable for complex datasets where you need to capture subtle patterns

Issues to consider:

The optimal alpha depends on the scale of your features and the complexity of your data
Very low alpha values may lead to numerical instability during optimization
The impact of alpha may vary depending on the loss function and penalty type used
Consider the trade-off between model complexity and generalization performance when tuning alpha

See Also