Introduction to Group Feature Selection
If you love data, you must love features.
Features in a Data Science project are the steps to your ladder, the water to your ship, the air to your airplane, the.. well, you got it.
Even though they are the bits of truth our model lives through they can’t all exist simultaneously, or at least they shouldn’t.
Some features can be irrelevant, poorly engineered, and (most likely) introduce multicollinearity that hurts our model’s accuracy and speed.
With the right features, you are 95% on the way to a model that achieves better accuracy, efficiency, and interpretability— Faster.
In this article, I will share my motivation for using existing feature selection methods and what made me engineer a whole (I think) new technique.
What do Data people usually do to select features?
Those are excellent methods, I use them separately or together all the time, but they introduce some technical issues:
To address these limitations, I recently started using a modified version of my favorite — Recursive methods- a modification that helps the process go faster while protecting the prime features.
Introducing…?Group Feature Selection.
Group Feature Selection is a method I used in my last Kaggle competition —?Parkinson’s Freezing of Gait Prediction?(My final position: 205/1379).
The main algorithm
Why is this method accurate?
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Why is this method fast?
When should I use it?
What are the main user-defined points, and how should I define them?
Some examples of the latter:
- All features that take the mean/median/std/min/max of other features.
- All features that take the lag (time series) of size X.
- All features that are model generated.
When shouldn’t you use it?
That is all for now!
I hope you will benefit from this approach in your next project, And may the features be with you.
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1 年Very cool and informative :) Thanks for that!