Using Recursive Feature Selection for Feature Elimination
So far, we have discussed two important evaluation metrics – the MAE and RMSE. We also saw how these metrics can be used with the help of the scikit-learn library and how a change in the values of these metrics can be used as an indicator of a feature's importance. However, if you have a large number of features, removing one feature at a time would become a very tedious job, and this is where RFE comes into the picture. When a dataset contains features (all columns, except the column that we want to predict) that either are not related to the target column or are related to other columns, the performance of the model can be adversely affected if all the features are used for model training. Let's understand the basic reasoning behind this.
For example, consider that you want to predict the number of sales of a product given the cost price of the product, the discount available, the selling price of the...
