| Classification | Regression |
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| Confusion Matrix, Accuracy Score, True Positive Rate, F1 Score, Precision , Recall | R-square & Adjusted R square |
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R^2 = 1 - SSres / SStot
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SSres = Sum of Residual or error -
sum(yi - yi^)2 (Square because we have negative value)
- 1.Here we found the best fit line
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SStot = Sum of average total = sum(Yi - Y^ avg)2 -
1.Here we found the average of best fit line then find the distance and then do summation.
| 1. Every time you add a independent variable to a model, the R-square increases, even if the independent variable is insignificant. It never declines. Whereas Adjusted R- squared increases only when independent variable is significant and affects dependept variable. |
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| 2. Adjusted r-squared value always be less than or equal to r-squared value. |