The Search for the Sweet Spot in a Linear Regression with Numeric Features
Consistent with the principle of Occam’s razor, starting simple often leads to the most profound insights, especially when piecing together a predictive model. In this post, using the Ames Housing Dataset, we will first pinpoint the key features that shine on their own. Then, step by step, we’ll layer these insights, observing how their combined effect enhances our ability to forecast accurately. As we delve deeper, we will harness the power of the Sequential Feature Selector (SFS) to sift through the complexities and highlight the optimal combination of features. This methodical approach will guide us to the “sweet spot” — a harmonious blend where the selected features maximize our model’s predictive precision without overburdening it with unnecessary data.
Let’s