python mean_absolute_error那个库 Python中的均值绝对误差(Mean Absolute Error) 在机器学习和数据分析中,模型的评估是一个重要环节,而均值绝对误差(Mean Absolute Error,MAE)是一种常用的评估指标。本文将介绍如何使用Python中的scikit-learn库来计算均值绝对误差,并提供相应的代码示例和图表以便更好地理解这个过程。
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(1) 均方差(mean_squared_error) (2) 平均绝对值误差(mean_absolute_error) (3) 可释方差得分(explained_variance_score) Explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. (4) 中值绝对误差(Median absolute error) (...
(1) 均方差(mean_squared_error) (2) 平均绝对值误差(mean_absolute_error) (3) 可释方差得分(explained_variance_score) Explained variation measures the proportion to which a mathematical model accounts for the variation (dispersion) of a given data set. (4) 中值绝对误差(Median absolute error) (...
def mean_absolute_error(y_true, y_pred): """计算平均绝对误差""" n = len(y_true) mae = sum(abs(y_true[i] - y_pred[i]) for i in range(n)) / n return mae # 测试 y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] ...
from sklearn.metrics import mean_squared_error,r2_score def evaluation(y_test, y_predict): mae = mean_absolute_error(y_test, y_predict) mse = mean_squared_error(y_test, y_predict) rmse = np.sqrt(mean_squared_error(y_test, y_predict)) ...
可以使用sklearn.metrics的mean_absolute_error()和mean_squared_error()方法来计算这些指标,如下面的代码片段所示: print(f'mse: {mse}') print(f'rmse: {rmse}') 上面脚本的输出结果如下: 可以使用score()方法直接计算R2: regrscore(X_test, y_test) ...
MAE(Mean Absolute Error):平均绝对误差 import numpy as np import matplotlib.pyplot as plt from sklearn import datasets 波士顿房产数据 """ Boston House Prices dataset === Notes --- Data Set Characteristics: :Number of Instances: 506 :Number of Attributes...
mean_absolute_error(y_true, y_pred) # ***均方误差(Mean squared error)*** fromsklearn.metrics import mean_squared_error y_true = [3, -0.5, 2, 7] y_pred = [2.5, 0.0, 2, 8] mean_squared_error(y_true, y_pred) # ***中值绝对误差(Median absolute...