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Regression Evaluation Metrics - Python Implementation

編輯:Python


Commonly used regression model evaluation indicatorspython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,mseJust open

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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0.9486081370449679

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整合

mapeSince there may be a denominator of 0的情況,So there are no statistics for now.Combine other metrics,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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