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], ...
predict(x_test_current_tmp) if len(values_TM)!=0: abs_error_train = 100.*mean_absolute_error(YTrain,y_pred_train)*len(YTrain)/(89.7* values_TM[0, 0] * values_TM[0,1]) print("abs train", abs_error_train) abs_error_test = 100.*mean_absolute_error(YTest,y_pred_test)*len...
atol: Python `float`-type indicating the admissible absolute error between analytical and sample statistics. """ x = math_ops.cast(dist.sample(num_samples, seed=seed), dtypes.float32) sample_mean = math_ops.reduce_mean(x, axis=0) sample_variance = math_ops.reduce_mean( math_ops.square...
mean(np.absolute(magnet_samples)) abs_magnetization_error = calculate_error(magnet_samples) print("<m> (<|M|/N>) = {0} +/- {1}".format(abs_magnetization, abs_magnetization_error)) magnetization = np.mean(magnet_samples) magnetization_error = calculate_error(magnet_samples) print("<M/...