### 计算预测模型的误差、精度、召回率和F1分数 在Python中，`scikit-learn`库提供了丰富的工具来计算各种性能指标。以下是具体的方法： #### 1. **误差** 误差可以通过多种方式定义，常见的有均方误差（MSE）、平均绝对误差（MAE）等。这些适用于回归问题。 ```python from sklearn.metrics import mean_squared_error, mean_absolute_error mse = mean_squared_error(y_true, y_pred) # 均方误差 mae = mean_absolute_error(y_true, y_pred) # 平均绝对误差 print(f"Mean Squared Error: {mse}") print(f"Mean Absolute Error: {mae}") ``` 对于分类问题，通常使用错误率作为误差度量，其公式为 \( \text{Error Rate} = 1 - \text{Accuracy} \)[^3]。 ```python accuracy = accuracy_score(y_true, y_pred) error_rate = 1 - accuracy print(f"Error Rate: {error_rate}") ``` --- #### 2. **精度 (Accuracy)** 精度是指被正确分类样本的比例，在不平衡数据集中可能会误导分析[^5]。 ```python from sklearn.metrics import accuracy_score accuracy = accuracy_score(y_true, y_pred) print(f"Accuracy: {accuracy:.4f}") ``` --- #### 3. **精确率 (Precision)** 和 **召回率 (Recall)** 精确率表示预测为正类中的真实正类比例；召回率则指实际正类中有多少被成功识别出来[^3]。 ```python from sklearn.metrics import precision_score, recall_score precision = precision_score(y_true, y_pred) recall = recall_score(y_true, y_pred) print(f"Precision: {precision:.4f}") # 精确率 print(f"Recall: {recall:.4f}") # 召回率 ``` --- #### 4. **F1分数** F1分数是精确率和召回率的调和平均值，综合反映了两者的平衡情况[^1]。 ```python from sklearn.metrics import f1_score f1 = f1_score(y_true, y_pred) print(f"F1 Score: {f1:.4f}") ``` --- #### 5. **平均准确率 (Average Precision)** 当需要评估整个精确率-召回率曲线时，可采用平均准确率这一指标[^2]。 ```python from sklearn.metrics import average_precision_score ap = average_precision_score(y_true, probas_pred) print(f"Average Precision: {ap:.4f}") ``` 注意：此处 `probas_pred` 需要是概率估计或决策函数的结果而非离散标签。 --- #### 综合示例代码 以下是一个完整的例子，展示如何一次性计算多个指标： ```python from sklearn.metrics import ( accuracy_score, precision_score, recall_score, f1_score, mean_squared_error, mean_absolute_error, average_precision_score ) # 假设已知的真实值和预测值分别为 y_true 和 y_pred # 对于连续型目标变量还需额外准备 probas_pred accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred) recall = recall_score(y_true, y_pred) f1 = f1_score(y_true, y_pred) mse = mean_squared_error(y_true, y_pred) mae = mean_absolute_error(y_true, y_pred) average_precision = average_precision_score(y_true, probas_pred) results = { 'Accuracy': round(accuracy, 4), 'Precision': round(precision, 4), 'Recall': round(recall, 4), 'F1-Score': round(f1, 4), 'Mean Squared Error': round(mse, 4), 'Mean Absolute Error': round(mae, 4), 'Average Precision': round(average_precision, 4) } for metric, value in results.items(): print(f"{metric}: {value}") ``` ---