Feature Selection

Feature Selection

event_note 28.06.2021

As part of the research used, for example, in the StockPicker application we have been researching the selection of variables enterings. When there are too many variables, the model has a worse ability to generalize and therefore is less robust and more prone to errors.

Why (goal)

Feature importance can provide insight into the dataset and show which features may be the most relevant to the target. This interpretation is important for domain experts and could be used as the basis or benchmark for gathering more and different additional data.

Feature importance can provide insight into the model. The most important features are calculated by a predictive model that has been fit on the dataset. Inspecting the importance score provides insight into that specific model and which features are the most important for it.

Feature importance can be used to improve a predictive model. This can be achieved by using the importance scores to select those features with the highest scores. This is a type of feature selection and can simplify the problem that is being modeled, speed up the modeling process (deleting features is called dimensionality reduction.

What (key points)

How (procedure)


Bar chart of Elastic Net Regression as Feature Importance Scores