Configure LogisticRegression "max_iter" Parameter

The max_iter parameter in scikit-learn’s LogisticRegression controls the maximum number of iterations taken for the solvers to converge.

Logistic Regression is a linear model used for binary classification problems. It predicts the probability of a binary outcome using a logistic function.

The max_iter parameter determines how many iterations the solver will run before stopping. If the solver does not converge within the specified number of iterations, it stops and reports a convergence warning.

The default value for max_iter is 100.

Common values for max_iter range from 100 to 1000, depending on the complexity of the data and the need for model convergence.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Generate synthetic dataset
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, 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 max_iter values
max_iter_values = [50, 100, 200, 500]
accuracies = []

for max_iter in max_iter_values:
    lr = LogisticRegression(max_iter=max_iter, random_state=42)
    lr.fit(X_train, y_train)
    y_pred = lr.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    accuracies.append(accuracy)
    print(f"max_iter={max_iter}, Accuracy: {accuracy:.3f}")

Running the example gives an output like:

max_iter=50, Accuracy: 0.770
max_iter=100, Accuracy: 0.770
max_iter=200, Accuracy: 0.770
max_iter=500, 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 LogisticRegression models with different max_iter values.
Evaluate the accuracy of each model on the test set.

Some tips and heuristics for setting max_iter:

Start with the default value of 100 and increase it if the model does not converge.
Higher max_iter values allow more iterations for the solver to converge, but excessively high values may lead to longer training times without significant performance gains.
Monitor the convergence warning to decide if an increase in max_iter is needed.

Issues to consider:

The optimal max_iter value depends on the complexity of the dataset and the convergence behavior of the solver.
Using too few iterations may result in non-convergence, while too many iterations may unnecessarily increase computational cost.
Balance between model convergence and training time is crucial for efficient model training.

See Also