The Probe Method: An Intuitive Feature Selection Technique

Real-world ML development is all about achieving a sweet balance between speed, model size, and performance.

We also saw this when we learned about different model compression techniques.

One common way to:

1. Improve speed,

2. Reduce size, and

3. Maintain (or minimally degrade) performance…

…is by using featuring selection.

As the name suggests, the idea is to select the most useful subset of features from the dataset.

While there are many many methods for feature selection, I have often found the “Probe Method” to be pretty reliable, practical and intuitive to use.

The animation below depicts how it works:

• Step 1) Add a random feature (noise).

• Step 2) Train a model on the new dataset.

• Step 3) Measure feature importance.

• Step 4) Discard original features that rank below the random feature.

• Step 5) Repeat until convergence.

This whole idea makes intuitive sense as well.

More specifically, if a feature’s importance is ranked below a random feature, it is probably a useless feature for the model.

This can be especially useful in cases where we have plenty of features, and we wish to discard those that don’t contribute to the model.

Of course, one shortcoming is that when using the Probe Method, we must train multiple models:

1. Train the first model with the random feature and discard useless features.

2. Keep training new models until the random feature is ranked as the least important feature (although typically, convergence does not result in plenty of models).

3. Train the final model without the random feature.

Nonetheless, the approach can be quite useful to reduce model complexity.

In fact, after selecting important features, we can further reduce the model size by using model compression techniques, which we discussed here: Model Compression: A Critical Step Towards Efficient Machine Learning.

👉 Over to you: What are some other popular feature selection techniques?

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