Mastering Predictive Analytics with scikit-learn and TensorFlow

Recursive feature elimination

The process of identifying important features and removing the ones that we think are not important for our model is called recursive feature elimination (RFE). RFE can also be applied in scikit-learn and we can use this technique for calculating coefficients, such as linear, logistic regression, or with models to calculate something called feature importance. The random forests model provides us with those feature importance metrics. So, for models that don't calculate either coefficients or feature importance, these methods cannot be used; for example, for KNN models, you cannot apply the RFE technique because this begins by predefining the required features to use in your model. Using all features, this method ...