Computing relative feature importance
When working with a dataset that contains N-dimensional data points, it must be understood that not all features are equally important. Some are more discriminative than others. If we have this information, we can use it to reduce the dimensionality. This is useful in reducing the complexity and increasing the speed of the algorithm. Sometimes, a few features are completely redundant. Hence, they can be easily removed from the dataset.
We will be using the AdaBoost regressor to compute feature importance. AdaBoost, short for Adaptive Boosting, is an algorithm that's frequently used in conjunction with other machine learning algorithms to improve their performance. In AdaBoost, the training data points are drawn from a distribution to train the current classifier. This distribution is updated iteratively so that the subsequent classifiers get to focus on the more difficult data points. The difficult data points are the ones that are misclassified...