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Fit a logistic regression to the Iris dataset and make predictions - Attempt 2
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| # Initialize logistic regression object and fit object | |
| lr_model2 = mlrose.LogisticRegression(algorithm = 'random_hill_climb', max_iters = 1000, | |
| bias = True, learning_rate = 0.01, | |
| early_stopping = True, clip_max = 5, max_attempts = 100, | |
| random_state = 3) | |
| lr_model2.fit(X_train_scaled, y_train_hot) | |
| # Predict labels for train set and assess accuracy | |
| y_train_pred = lr_model2.predict(X_train_scaled) | |
| y_train_accuracy = accuracy_score(y_train_hot, y_train_pred) | |
| print('Training accuracy: ', y_train_accuracy) | |
| # Predict labels for test set and assess accuracy | |
| y_test_pred = lr_model2.predict(X_test_scaled) | |
| y_test_accuracy = accuracy_score(y_test_hot, y_test_pred) | |
| print('Test accuracy: ', y_test_accuracy) |
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