You might have noticed that I start each Tuesday Tip with a link of the week and end with a humorous/interesting P.S.
If you ever want to nominate a link for either category, please feel free to share it with me! 💌
🔗 Link of the week
This is not only a fascinating read, but also an excellent case study in the challenges of real-world data gathering and data analysis!
👉 Tip #27: Improve your model with automated feature selection
Recently, a reader asked me how to get “un-stuck” with his Data Science project, given that he’s facing the following challenges:
- Irrelevant features: How to select the right features for analysis and modeling?
- High-dimensional data: Best practices for dealing with datasets with a large number of features.
What he needs is “feature selection”, which is the process of removing uninformative features from your model. These are features that are NOT helping your model to make better predictions. In other words, uninformative features are adding “noise” to your model, rather than “signal”. 📡
Here’s how your model can benefit from feature selection:
- Model accuracy is often improved by removing uninformative features.
- Models are generally easier to interpret when they include fewer features.
- When you have fewer features, models will take less time to train, and it may cost less to gather and store the data that is required to train them.
Methods for feature selection
There are many valid methods for feature selection, including human intuition, domain knowledge, and data exploration. But for the moment, I want to focus on automated feature selection that can be included in a scikit-learn Pipeline. ⚡
Within the category of automated feature selection, there are subcategories such as intrinsic methods (like L1 regularization) and wrapper methods (like recursive feature elimination), though the most flexible and computationally efficient methods are in the subcategory of filter methods (like SelectPercentile).
As you might guess, automated feature selection is a vast and complex topic! However, I’ll give you a quick introduction to one of these categories so that you can get started today! 🚀
A quick introduction to “filter methods”
A filter method starts by scoring every single feature to quantify its potential relationship with the target column. Then, the features are ranked by their scores, and only the top scoring features are passed to the model. 🏅
Thus, they’re called filter methods because they filter out what they believe to be the least informative features and then pass on the more informative features to the model.
Filter methods vary in terms of the processes they use to score the features. For example:
- SelectPercentile scores features using univariate statistical tests
- SelectFromModel scores features using the coefficients or feature importances of a model
In each case, you have to select how many features are passed to the prediction model by setting a percentile (for SelectPercentile) or a scoring threshold (for SelectFromModel). And of course, these parameters should be tuned using a grid search! 🔎
Using feature selection in scikit-learn
Despite the conceptual complexity, it’s surprisingly simple to add automated feature selection to a scikit-learn Pipeline. I’ll show you how:
🔗 Here’s my code from the video (Jupyter notebook)
Want to learn more about feature selection?
Feature selection is a huge topic, but I cover it in detail in Chapter 13 of my upcoming course:
🔗 Master Machine Learning with scikit-learn (Data School course)
I’ve been working on this course for YEARS, and I’m planning to release the first 16 chapters by the end of 2023! Stay tuned for the launch announcement... 👂
In the meantime, my top recommendation for learning about feature selection is this comprehensive book:
🔗 Feature Engineering and Selection (free online book)
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See you next Tuesday!
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