Machine Learning

Neural networks achieve state-of-the-art accuracy in many fields such as computer vision, natural-language processing, and reinforcement learning. In this tutorial, you’ll specifically explore two types of explanations: 1. Saliency maps, which highlight the most important parts of the input image; and 2. decision trees, which break down each prediction into a sequence of intermediate decisions.

In this article we’ll start with an introduction to gradient boosting for regression problems, what makes it so advantageous, and its different parameters. Then we’ll implement the GBR model in Python, use it for prediction, and evaluate it.