回歸評估指標-Python實現

常用回歸模型評估指標的python計算邏輯。python和相關package版本：

import
sys


import
sklearn


import
pandas
as
pd


import
numpy
as
np


import
math


print(
"Python版本：",
sys.
version)


print(
"sklearn版本：",
sklearn.
__version__)


print(
"pandas：",
pd.
__version__)


print(
"numpy：",
np.
__version__)


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Python版本： 3.7.0 (default, Jun 28 2018, 08:04:48) [MSC v.1912 64 bit (AMD64)]

sklearn版本： 0.22

pandas： 0.23.4

numpy： 1.17.4


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MAE

from
sklearn.
metrics
import
mean_absolute_error


y_true
= [
3,
-
0.5,
2,
7]


y_pred
= [
2.5,
0.0,
2,
8]


mean_absolute_error(
y_true,
y_pred)


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0.5


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sum([
abs(
y_true[
i]
-
y_pred[
i])
for
i
in
range(
len(
y_true))])
/
len(
y_true)


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0.5


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MSE

from
sklearn.
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)


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0.375


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sum([
abs(
y_true[
i]
-
y_pred[
i])
*
*
2
for
i
in
range(
len(
y_true))])
/
len(
y_true)


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0.375


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RMSE

這個沒啥說的，mse開方就行

math.
sqrt(
mean_squared_error(
y_true,
y_pred))


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0.6123724356957945


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R方

from
sklearn.
metrics
import
r2_score


r2_score(
y_true,
y_pred)


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0.9486081370449679


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1
-
sum([(
y_true[
i]
-
y_pred[
i])
*
*
2
for
i
in
range(
len(
y_true))])
/
sum(

 [(
y_true[
i]
-
sum(
y_true)
/
len(
y_true))
*
*
2
for
i
in
range(
len(
y_true))])


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2.

0.9486081370449679


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整合

mape由於可能存在分母為0的情況，因此暫時沒有統計。把其他指標整合一下，格式化輸出：

from
sklearn.
metrics
import
mean_absolute_error


from
sklearn.
metrics
import
mean_squared_error


from
sklearn.
metrics
import
r2_score


def
reg_metric(
y_true,
y_predict):


mae
=
mean_absolute_error(
y_true,
y_pred)


mse
=
mean_squared_error(
y_true,
y_pred)


rmse
=
math.
sqrt(
mean_squared_error(
y_true,
y_pred))


r2
=
r2_score(
y_true,
y_pred)


print(
"MAE:",
mae)


print(
"MSE:",
mse)


print(
"RMSE:",
rmse)


print(
"R Square:",
r2)


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reg_metric(
y_true
=
y_true,
y_predict
=
y_pred)


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MAE: 0.5

MSE: 0.375

RMSE: 0.6123724356957945

R Square: 0.9486081370449679


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2020-06-16、連綿梅雨季 於南京市江寧區九龍湖