Random Logistic Regression Accuracy Evaluation

This function uses the LogisticRegression model from the scikit-learn library to train a dataset and evaluate its accuracy on a test set.

Technology Stack : scikit-learn, LogisticRegression, train_test_split, accuracy_score

Code Type : Machine learning

Code Difficulty : Intermediate

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

def random_logistic_regression(X, y, test_size=0.2):
    """
    Trains a logistic regression model and evaluates its accuracy on a test set.
    """
    # Split the data into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=42)
    
    # Initialize the logistic regression model
    model = LogisticRegression()
    
    # Train the model on the training data
    model.fit(X_train, y_train)
    
    # Make predictions on the test data
    y_pred = model.predict(X_test)
    
    # Calculate the accuracy of the model
    accuracy = accuracy_score(y_test, y_pred)
    
    return accuracy