Selecting features
If we want to be nice to our machine learning algorithm, we will provide it with features that are not dependent on each other, yet highly dependent on the value to be predicted. It means that each feature adds some 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 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 from. Just think of the classification task where we had a bag-of-words to classify the quality of an answer, which would require a 1,000 by 1,000 scatter plot. In this case, we need a more automated way to detect the overlapping features and...
