Regression Metrics Calculation Made easy

Last update: Jan 02, 2023

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Overview

Regression Metrics

Mean Absolute Error
Mean Square Error
Root Mean Square Error
Root Mean Square Logarithmic Error
Root Mean Square Logarithmic Error with negative value handle
R2 Score
Adjusted R2 Score
Mean Absolute Percentage Error

from regmetrics.metrics import *
y_true = [3, 0.5, 2, 7]
y_pred = [2.5, 0.0, 2, -8]


print("R2Score: ",r2(y_true, y_pred))
print("Adjusted_R2_Score:",adj_r2(y_true, y_pred))
print("RMSE:", rmse(y_true, y_pred))
print("MAE:",mae(y_true, y_pred))
print("RMSLE with Neg Value:", rmsle_with_negval(y_true, y_pred))
print("MSE:", mse(y_true, y_pred))
print("MAPE: ", mape(y_true, y_pred))

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Comments

Very nice toolkit

This isn't really an issue. I wanted to thank you for sharing such a nice toolkit for regression tasks with tensorflow

Do you have a similar toolkit for classification?

opened by happypanda5 0

Releases(v1.4.0)

v1.4.0(Oct 30, 2021)
Changelog for v1.4.0 (2022-01-13)

Name clashes resolved with keras names

Changelog for v1.3.0 (2021-11-18)

new regresson metrics are added with details explaination

Changelog for v1.2.0 (2021-10-31)

Adjusted r2 score error solved

Changelog for v1.1.0 (2021-10-31)

SomeError solved

Changelog for v1.0.0 (2021-10-31)

regressionmetrics package first release 1.0.0.

Source code(tar.gz)
Source code(zip)

Owner

Ashish Patel

AI Researcher & Senior Data Scientist at Softweb Solutions Avnet Solutions(Fortune 500) | Rank 3 Kaggle Kernel Master

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