Regression Metrics Calculation Made easy

Related tags

General Utilitiesregmetrics
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

    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
Ashish Patel
GitHub Repository
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