Feature Generation with Gradient Boosted Decision Trees

 By Medium - 2021-03-22

Description

In this blog, we implement an idea previously mentioned in a Facebook AI research paper: Practical Lessons from Predicting Clicks on Ads at Facebook The idea is to create non-linear transformations…

Summary

  • TL;DR The idea is to create non-linear transformations of features using a gradient boosting decision tree that is then used to predict with a final estimator.
  • pip install sktools And some code snippet to integrate to reproduce it Introduction During the modeling process of a Machine Learning task, there exists the need of creating new features that describe the problem and allow the model to achieve better performance, this is the so-called “feature engineering process ”.
  • Given any data set, after training a GBM each instance can be codified depending on which leaf it felt from each tree.

 

Topics

  1. Machine_Learning (0.37)
  2. Backend (0.11)
  3. NLP (0.08)

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