Since my data had some outliers I wanted a model that is less sensitive to them. I tried Huber Regression today after experimenting with a couple of other regression models.  Although I obtained lower R-squared values from cross-validation which may indicate that my Huber Regression model is not explaining as much of the variance in the data as my Multiple Linear Regression model made earlier, I found it to be performing better in the presence of outliers.

The dilemma is whether I want a model that is less sensitive to outliers, the one that’s capturing the overall trend of the data is more critical, the Multiple Linear Regression model might be more suitable.

Also, while R-squared is just one metric, it’s essential to consider other evaluation metrics MSE and MAE to get a holistic view of predictive performance.