Attribute noise

There are a few methods to reduce attribute noise in large datasets. One of these methods is an algorithm named the Pairwise Attribute Noise Detection Algorithm (PANDA). PANDA compares features pairwise and identifies which of them adds noise to the dataset. It is a very effective algorithm, but unfortunately very computationally heavy. If our dataset had a few hundred features (which is when we would really need to use this algorithm), we would need a lot of computational power to identify these features that bring in little to the analysis.

Fortunately, there are machine learning algorithms that provide similar functionality with little computational overhead. One of these algorithms is the random forest algorithm, which allows you to retrieve the set of feature importance values. These values are a way of identifying which features are not used in any of the decision trees in this forest.

Let us then see how to use that algorithm to extract and visualize the...