The very naive way of evaluating a model is by considering the R-Squared value. Suppose if I get an R-Squared of 95%, is that good enough? Through this blog, Let us try and understand the ways to evaluate your regression model.

Evaluation metrics;

• Mean/Median of prediction
• Standard Deviation of prediction
• Range of prediction
• Coefficient of Determination (R2)
• Relative Standard Deviation/Coefficient of Variation (RSD)
• Relative Squared Error (RSE)
• Mean Absolute Error (MAE)
• Relative Absolute Error (RAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error on Prediction (RMSE/RMSEP)
• Normalized Root Mean Squared Error (Norm RMSEP)
• Relative Root Mean Squared Error (RRMSEP)

#rmse #machine-learning #regression #data-science #metrics