Consider the updated script run_regressor_evaluation.py.
In the first step, we extract the evaluation logic into a function.
def evaluate_model(model, X_train, y_train, X_test, y_test):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
mae = metrics.mean_absolute_error(y_test, y_pred)
print(f"{model}: MAE={mae:.1f}")This allows us to reuse the function within this script (and perhaps beyond it), eliminating code duplication and, through the single point of definition, increasing maintainability.
# evaluate models
evaluate_model(LogisticRegression(solver='lbfgs', max_iter=1000), X_train, y_train, X_test, y_test)
evaluate_model(KNeighborsRegressor(n_neighbors=1), X_train, y_train, X_test, y_test)
evaluate_model(RandomForestRegressor(n_estimators=100), X_train, y_train, X_test, y_test)
evaluate_model(DecisionTreeRegressor(random_state=42, max_depth=2), X_train, y_train, X_test, y_test)