Configure SGDClassifier "validation_fraction" Parameter

The validation_fraction parameter in scikit-learn’s SGDClassifier determines the proportion of training data to set aside as a validation set for early stopping.

Stochastic Gradient Descent (SGD) is an optimization method used in various machine learning algorithms. Early stopping is a technique to prevent overfitting by monitoring the model’s performance on a validation set during training.

The validation_fraction parameter controls how much of the training data is used for early stopping validation. A larger value provides more data for validation but reduces the amount of data available for training.

The default value for validation_fraction is 0.1, meaning 10% of the training data is used for validation. Common values range from 0.1 to 0.3, depending on the dataset size 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

# Generate synthetic dataset
X, y = make_classification(n_samples=10000, n_features=20, n_informative=10,
                           n_redundant=5, n_classes=2, 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 validation_fraction values
val_fractions = [0.1, 0.2, 0.3]
accuracies = []

for val_frac in val_fractions:
    sgd = SGDClassifier(loss='log_loss', max_iter=1000, tol=1e-3,
                        validation_fraction=val_frac, n_iter_no_change=5,
                        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"validation_fraction={val_frac}, Accuracy: {accuracy:.3f}, "
          f"Iterations: {sgd.n_iter_}")

Running the example gives an output like:

validation_fraction=0.1, Accuracy: 0.795, Iterations: 76
validation_fraction=0.2, Accuracy: 0.795, Iterations: 76
validation_fraction=0.3, Accuracy: 0.795, Iterations: 76

The key steps in this example are:

Generate a synthetic binary classification dataset
Split the data into train and test sets
Train SGDClassifier models with different validation_fraction values
Evaluate the accuracy of each model on the test set
Compare the number of iterations and final accuracy for each configuration

Some tips and heuristics for setting validation_fraction:

Start with the default value of 0.1 and adjust based on dataset size and model performance
Use a larger fraction for smaller datasets to ensure a representative validation set
Consider using cross-validation instead for very small datasets

Issues to consider:

A larger validation_fraction reduces the amount of data available for training
Too small a validation set may not provide reliable early stopping signals
The optimal value depends on the dataset size, complexity, and the specific problem

See Also