Configure SGDClassifier "random_state" Parameter

The random_state parameter in scikit-learn’s SGDClassifier controls the randomness of the stochastic gradient descent algorithm during training.

Stochastic Gradient Descent (SGD) is an optimization algorithm that updates model parameters incrementally using subsets of the training data. The random_state parameter affects the shuffling of the training data and the initialization of the model’s weights.

Setting random_state to a fixed value ensures reproducibility of results across different runs. This is crucial for debugging, comparing models, and producing consistent predictions.

The default value for random_state is None, which uses the system’s random number generator. For reproducibility, it’s common to use integer values (e.g., 42, 0, 1000).

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, 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 random_state values
random_state_values = [None, 0, 42, 100]
accuracies = []

for rs in random_state_values:
    sgd = SGDClassifier(random_state=rs, 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"random_state={rs}, Accuracy: {accuracy:.3f}")

# Train multiple times with random_state=None
print("\nMultiple runs with random_state=None:")
for _ in range(3):
    sgd = SGDClassifier(random_state=None, 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)
    print(f"Accuracy: {accuracy:.3f}")

random_state=None, Accuracy: 0.705
random_state=0, Accuracy: 0.800
random_state=42, Accuracy: 0.770
random_state=100, Accuracy: 0.770

Multiple runs with random_state=None:
Accuracy: 0.740
Accuracy: 0.755
Accuracy: 0.775

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 random_state values
Evaluate the accuracy of each model on the test set
Demonstrate the variability of results when random_state is None

Tips for setting random_state:

Use a fixed integer value for reproducibility in experiments and production
Keep the same random_state value across model training and evaluation steps
Document the random_state value used in your experiments for future reference

Issues to consider:

Different random_state values may lead to slightly different model performance
Using None can make debugging and model comparison challenging due to inconsistent results
In some cases, averaging results over multiple runs with different random states can provide more robust performance estimates

See Also