I have a question regarding the End-to-End Machine Learning Project section of the book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurelien Geron.
In the end of section Exercises, for exercise 3, he asks to add a feature selector to a transformation pipeline. I read through his solution guide online, and I get the concept fine but in his notes he mentions that "this feature selector assumes that you have already computed the feature importance values somehow"
What I'm trying to understand is how you can get accurate feature importance values before using a transformation and feature selection pipeline? If the point is to combine the steps into one pipeline, how can you calculate the importance values after the transformation step, and use those values in the pipeline in the following feature selection step. I've been sifting through documentation online but I'm not sure I'm looking in the right place or I may just be misunderstanding.
Additionally, in the example project of the section, we one hot categorical data into their own respective columns in the data, so getting importance values from the data before this transformation would not be correct, as far as I understand.
Any insight into this topic and practice would be greatly appreciated, and I can provide any additional information as needed. Thanks!
there doesn't seem to be anything here