下面是一个简单的示例,演示如何使用scikit-learn库计算MAE。 importnumpyasnpfromsklearn.metricsimportmean_absolute_error# 实际值y_true=np.array([3,-0.5,2,7])# 预测值y_pred=np.array([2.5,0.0,2,8])# 计算均值绝对误差mae=mean_absolute_error(y_true,y_pred)print("均值绝对误差 (MAE):",mae)...
python实现Symmetric Mean Absolute Percentage Error Python实现对称平均绝对百分比误差(Symmetric Mean Absolute Percentage Error) 在机器学习和统计分析中,评估预测模型的性能是非常重要的工作。对称平均绝对百分比误差(Symmetric Mean Absolute Percentage Error,简称SMAPE)是一种用于评估预测准确性的指标。本篇文章将帮助初学...
* Added mean absolute error in linear regression * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Code feedback changes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * ...
sklearn.metrics.mean_absolute_error — scikit-learn 1.3.2 documentation mean_absolute_error函数使用案例 案例1 from sklearn.metrics import mean_absolute_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] print(mean_absolute_error(y_true, y_pred)) y_true = [[0.5, 1], ...
>>>fromsklearn.metricsimportmean_absolute_error>>>y_true = [3,-0.5,2,7]>>>y_pred = [2.5,0.0,2,8]>>>mean_absolute_error(y_true, y_pred)0.5>>>y_true = [[0.5,1], [-1,1], [7,-6]]>>>y_pred = [[0,2], [-1,2], [8,-5]]>>>mean_absolute_error(y_true, y_pre...
tf.keras.losses.MeanAbsoluteError( reduction=losses_utils.ReductionV2.AUTO, name='mean_absolute_error') 参数 reduction类型tf.keras.losses.Reduction适用于损失。默认值为AUTO.AUTO表示缩减选项将由使用上下文确定。对于几乎所有情况,这默认为SUM_OVER_BATCH_SIZE.当与tf.distribute.Strategy,在内置训练循环之外,...
(array-like): True values.y_pred (array-like): Predicted values.Returns:float: The NMAE value."""mae=mean_absolute_error(y_true,y_pred)returnmae / np.mean(y_true)# Example usagey_true=np.array([3,-0.5,2,7])y_pred=np.array([2.5,0.0,2,8])nmae=normalized_mean_absolute_error(y...
例如：mean_absolute_error给出val_mean_absolute_errormae给出val_maeaccuracy给出val_accuracyacc给出...
mean_squared_error(y_test, predictions) mean_absolute_error = skmetrics.mean_absolute_error(y_test, predictions) result = {'mean_squared_error': mean_squared_error, 'mean_absolute_error': mean_absolute_error} return result Example #11
(powers+1.0)# Produces a relative absolute error of 1.7%.self.assertAllClose(integral.eval(),true_value.eval(),rtol=0.02)# Now skip the first 1000 samples and recompute the integral with the next# thousand samples. The sequence_indices argument can be used to do this.sequence_indices=...