Configure SGDClassifier "shuffle" Parameter

The shuffle parameter in scikit-learn’s SGDClassifier determines whether the training data is shuffled before each epoch.

Stochastic Gradient Descent (SGD) is an iterative optimization algorithm that updates model parameters based on individual training samples. The shuffle parameter controls whether these samples are presented in a random order.

Shuffling can improve convergence speed and final model performance by reducing the impact of data ordering on parameter updates. It helps prevent the model from learning spurious patterns that may exist in the original data order.

The default value for shuffle is True. In practice, it’s often beneficial to keep shuffling enabled unless there’s a specific reason to maintain the original data order.

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=1000, 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 shuffle=True
sgd_shuffle = SGDClassifier(random_state=42, max_iter=100)
sgd_shuffle.fit(X_train, y_train)
y_pred_shuffle = sgd_shuffle.predict(X_test)
accuracy_shuffle = accuracy_score(y_test, y_pred_shuffle)

# Train with shuffle=False
sgd_no_shuffle = SGDClassifier(shuffle=False, random_state=42, max_iter=100)
sgd_no_shuffle.fit(X_train, y_train)
y_pred_no_shuffle = sgd_no_shuffle.predict(X_test)
accuracy_no_shuffle = accuracy_score(y_test, y_pred_no_shuffle)

print(f"Accuracy with shuffle=True: {accuracy_shuffle:.3f}")
print(f"Accuracy with shuffle=False: {accuracy_no_shuffle:.3f}")

Running the example gives an output like:

Accuracy with shuffle=True: 0.770
Accuracy with shuffle=False: 0.770

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 shuffle=True and shuffle=False
Evaluate and compare the accuracy of each model on the test set

Tips for using the shuffle parameter:

Keep shuffling enabled (default) for most cases to improve convergence and generalization
If your data has a meaningful temporal or sequential order, consider disabling shuffling
Use a fixed random state for reproducibility when shuffling is enabled

Issues to consider:

Shuffling adds some computational overhead, which may be noticeable for very large datasets
The impact of shuffling can vary depending on the nature of your data and the specific problem
When shuffling is disabled, the model may be more sensitive to the initial order of the training data

See Also