### Python 中监督学习模型评估的目的 #### 准确衡量模型性能 为了确保所构建的机器学习模型能够有效工作，在开发过程中需要对模型进行全面测试。这不仅涉及验证模型能否正确处理已知数据集，更重要的是要确认其对未来未知数据同样具备良好的泛化能力[^2]。 #### 防止过拟合现象发生 当一个模型过度适应训练样本而丧失了对外部新数据的良好表现时，则称为发生了过拟合。有效的评估可以帮助识别并解决这一问题，从而提高实际应用价值[^1]。 #### 支持决策制定过程 通过对多个候选方案之间比较分析，可以依据具体业务需求挑选最优解；同时也能指导后续调参方向，促进迭代优化进程[^3]。 #### 提供反馈机制给开发者 每次实验后的评价结果都将成为调整参数配置的重要参考依据之一，有助于持续改进算法设计思路与框架结构[^5]。 ```python from sklearn.model_selection import train_test_split, cross_val_score from sklearn.metrics import accuracy_score, mean_squared_error import numpy as np def evaluate_model(model, X, y): """Evaluate a given model using k-fold cross-validation.""" # Split data into training and testing sets (80% for training, 20% for validation) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Train the classifier on the training set model.fit(X_train, y_train) # Predict labels for both datasets predictions_train = model.predict(X_train) predictions_test = model.predict(X_test) # Calculate accuracies or errors depending on task type if isinstance(y[0], str): # Classification problem acc_train = accuracy_score(predictions_train, y_train) acc_test = accuracy_score(predictions_test, y_test) print(f'Training Accuracy: {acc_train:.4f}') print(f'Validation Accuracy: {acc_test:.4f}') else: # Regression problem mse_train = mean_squared_error(predictions_train, y_train) mse_test = mean_squared_error(predictions_test, y_test) rmse_train = np.sqrt(mse_train) rmse_test = np.sqrt(mse_test) print(f'Training RMSE: {rmse_train:.4f}') print(f'Validation RMSE: {rmse_test:.4f}') # Example usage with dummy dataset if __name__ == "__main__": from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression # Generate synthetic binary classification dataset X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42) lr_clf = LogisticRegression() evaluate_model(lr_clf, X, y) ```