Configure SGDClassifier "power_t" Parameter

The power_t parameter in scikit-learn’s SGDClassifier controls the learning rate decay during training iterations.

SGDClassifier uses stochastic gradient descent for optimization, updating the model’s parameters after each sample. The power_t parameter determines how quickly the learning rate decreases over time.

Higher values of power_t cause the learning rate to decay more rapidly, while lower values result in slower decay. This can significantly impact the model’s convergence and final performance.

The default value for power_t is 0.5, which corresponds to an inverse scaling learning rate. Common values range from 0 to 1.

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 different power_t values
power_t_values = [0.1, 0.5, 0.9]
accuracies = []

for power_t in power_t_values:
    sgd = SGDClassifier(power_t=power_t, random_state=42, max_iter=1000)
    sgd.fit(X_train, y_train)
    y_pred = sgd.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    print(f"power_t={power_t}, Accuracy: {accuracy:.3f}")

Running the example gives an output like:

power_t=0.1, Accuracy: 0.770
power_t=0.5, Accuracy: 0.770
power_t=0.9, Accuracy: 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 different power_t values
Evaluate the accuracy of each model on the test set

Some tips and heuristics for setting power_t:

Start with the default value of 0.5 and adjust based on model performance
Lower values (e.g., 0.1) may work better for non-stationary problems
Higher values (e.g., 0.9) can lead to faster initial convergence but may settle on a suboptimal solution

Issues to consider:

The optimal power_t value depends on the specific dataset and problem
Very low values may lead to slow convergence, while very high values might cause the model to converge prematurely
Consider using learning_rate='optimal' instead, which automatically adjusts the learning rate

See Also