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Building Machine Learning Systems with Python - Second Edition by Luis Pedro Coelho, Willi Richert

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Selecting features

If we want to be nice to our machine learning algorithm, we provide it with features that are not dependent on each other, yet highly dependent on the value to be predicted. This means that each feature adds salient information. Removing any of the features will lead to a drop in performance.

If we have only a handful of features, we could draw a matrix of scatter plots (one scatter plot for every feature pair combination). Relationships between the features could then be easily spotted. For every feature pair showing an obvious dependence, we would then think of whether we should remove one of them or better design a newer, cleaner feature out of both.

Most of the time, however, we have more than a handful of features to choose ...

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