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沙发
楼主 |
发表于 2019-9-12 08:18:10
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- import numpy as np
- from math import sqrt
- def accuracy_score(y_true, y_predict):
- """计算y_true和y_predict之间的准确率"""
- assert len(y_true) == len(y_predict), \
- "the size of y_true must be equal to the size of y_predict"
- return np.sum(y_true == y_predict) / len(y_true)
- def mean_squared_error(y_true, y_predict):
- """计算y_true和y_predict之间的MSE"""
- assert len(y_true) == len(y_predict), \
- "the size of y_true must be equal to the size of y_predict"
- return np.sum((y_true - y_predict)**2) / len(y_true)
- def root_mean_squared_error(y_true, y_predict):
- """计算y_true和y_predict之间的RMSE"""
- return sqrt(mean_squared_error(y_true, y_predict))
- def mean_absolute_error(y_true, y_predict):
- """计算y_true和y_predict之间的RMSE"""
- assert len(y_true) == len(y_predict), \
- "the size of y_true must be equal to the size of y_predict"
- return np.sum(np.absolute(y_true - y_predict)) / len(y_true)
- def r2_score(y_true, y_predict):
- """计算y_true和y_predict之间的R Square"""
- return 1 - mean_squared_error(y_true, y_predict)/np.var(y_true)
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