logistic_regression.py

from sklearn.linear_model import LogisticRegression as lr
from sklearn.metrics import accuracy_score

import numpy as np

from decision_tree import load_data


if __name__ == '__main__':
    print ("Tutorial: Training a logistic regression to detect phishing websites")

    # Load the training data
    train_inputs, train_outputs, test_inputs, test_outputs = load_data()
    print ("Training data loaded.")

    # Create a logistic regression classifier model using scikit-learn
    classifier = lr()
    print ("Logistic regression classifier created.")

    print ("Beginning model training.")
    # Train the logistic regression classifier
    classifier.fit(train_inputs, train_outputs)
    print ("Model training completed.")

    # Use the trained classifier to make predictions on the test data
    predictions = classifier.predict(test_inputs)
    print ("Predictions on testing data computed.")

    # Print the accuracy (percentage of phishing websites correctly predicted)
    accuracy = 100.0 * accuracy_score(test_outputs, predictions)
    print ("The accuracy of your logistic regression on testing data is: " + str(accuracy))