# Evaluation Metrics for Regression Problems: Quick examples + Reference

Last updated:- RMSE
- MAE (Mean Squared Error)
- R2 (r-squared)
- MAPE (Mean absolute percentage error)
- Median Absolute Error
- Spearman's Rho

WIP Alert This is a work in progress. Current information is correct but more content may be added in the future.

For evaluation metrics to be used in

classificationproblems, see instead: Evaluation Metrics for Classification Problems: Quick Examples + References

## RMSE

```
TODO
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## MAE (Mean Squared Error)

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TODO
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## R^{2} (r-squared)

R^{2} is one of several **goodnes-of-fit** measures.

It measures how much of the variance of the target variable is explainable by the trained model.

$$ R^2 = \frac{\text{Variance (of target variable) which can be explained by the model}}{\text{total variance of the target variable}} $$

R-squared ranges between 0 (model doesn't explain any variance) and 1 (all variance of the target variable is explainable by the model)

Biased models (i.e. ones that consistently err "up" or "down")

*can*still have high R-squared values!

## MAPE (Mean absolute percentage error)

## Median Absolute Error

```
TODO http://scikit-learn.org/stable/modules/model_evaluation.html#median-absolute-error
```

## Spearman's Rho

```
TODO
```