缩写 | 英文 | 中文 |
RMSR | Root Mean Squared Error | 均方根误差 |
MAE | Mean Absolute Error | 平均绝对误差 |
python实现代码
# -*- coding: utf-8 -*-
import math
def get_average(records):
"""
平均值
"""
return sum(records) / len(records)
def get_variance(records):
"""
方差 反映一个数据集的离散程度
"""
average = get_average(records)
return sum([(x - average) ** 2 for x in records]) / len(records)
def get_standard_deviation(records):
"""
标准差 == 均方差 反映一个数据集的离散程度
"""
variance = get_variance(records)
return math.sqrt(variance)
def get_rms(records):
"""
均方根值 反映的是有效值而不是平均值
"""
return math.sqrt(sum([x ** 2 for x in records]) / len(records))
def get_mse(records_real, records_predict):
"""
均方误差 估计值与真值 偏差
"""
if len(records_real) == len(records_predict):
return sum([(x - y) ** 2 for x, y in zip(records_real, records_predict)]) / len(records_real)
else:
return None
def get_rmse(records_real, records_predict):
"""
均方根误差：是均方误差的算术平方根
"""
mse = get_mse(records_real, records_predict)
if mse:
return math.sqrt(mse)
else:
return None
def get_mae(records_real, records_predict):
"""
平均绝对误差
"""
if len(records_real) == len(records_predict):
return sum([abs(x - y) for x, y in zip(records_real, records_predict)]) / len(records_real)
else:
return None
if __name__ == '__main__':
records1 = [3, 4, 5]
records2 = [2, 4, 6]
# 平均值
average1 = get_average(records1) # 4.0
average2 = get_average(records2) # 4.0
# 方差
variance1 = get_variance(records1) # 0.66
variance2 = get_variance(records2) # 2.66
# 标准差
std_deviation1 = get_standard_deviation(records1) # 0.81
std_deviation2 = get_standard_deviation(records2) # 1.63
# 均方根
rms1 = get_rms(records1) # 4.08
rms2 = get_rms(records2) # 4.32
# 均方误差
mse = get_mse(records1, records2) # 0.66
# 均方根误差
rmse = get_rmse(records1, records2) # 0.81
# 平均绝对误差
mae = get_mae(records1, records2) # 0.66
公式参考：
方差（variance）、标准差（Standard Deviation）、均方差、均方根值（RMS）、均方误差（MSE）、均方根误差（RMSE）
