Which Metric to consider for Regression R2 or RMSE



Hi All,

We are working on Regression problem usinng Sklearn, So what should be best Metric for this R2 or RMSE or any other.

We are getting R2 in range of 0.35 , is it fine or not.



Adjusted R2 is the best metric.
Value around 0.4-0.5 is good.



RMSE is root mean squared error. It is based the assumption that data error follow normal distribution.

This is a measure of the average deviation of model predictions from the actual values in the dataset.

R2 is coefficient of determination, scaled between 0 and 1.

R^2 = 1-(SSE/SST)

SSE : sum of squared error, SST : total sum of squares

R-squared is simply the fraction of response variance that is captured by the model.

If R-squared = 1, means the model fits the data perfectly.

while both indicate the goodness of the fit, R-squared can be more easily interpreted.

It directly measures the goodness of fit in capturing the variance in training data.

For example : if R2=0.7, it says that with this model, we can explain 70% of what is going on in the real data,

rest 30% can’t be explained.

if your R2 is in the range 0.35, then model explains only 35% of the variance. This is not a good fit.

something in the range 0.7-0.8, is a good model.

I hope this helps.


Nicely explained @himansu979,
One more comment regarding R2 and Adjusted-R2 : -

R2 increases as we add more number of explanatory variables into the model. Thus, to correct for this situation - Adjusted-R2 is measured in following way:

Adjusted-R2 = 1 - [(N-1)/(N-d)](1 - R2)

Where, N = number of data points. (d + 1) = number of parameters in the model.