Photo by Samrat Khadka on Unsplash
Overview
Estimated Reading Time
Plan on around 90 - 120 minutes for this preparation reading, which consists of a mix of textbook and online reading.
The objective of this module is to provide a real-world scenario in which you can practice the following data science / machine learning skills:
- Gradient Boosted Trees and the XGBoost Library
- Evaluating how well a model carries out regression
Preparation Reading
Model Ensembles
First, read this section from your textbook:
- Read Section 8.4.5 of your text (Performance Measures: Continuous Targets)
- Read section 4.4.5 of your text (Model Ensembles)
Gradient Boosted Trees
Four videos are listed below.
The first video explains the concepts of gradient boosted trees within the context of regression tasks. The second explains the mathematics behind those concepts.
The third video explains the concepts of gradient boosted trees within the context of classification tasks. The fourth explains the mathematics behind those concepts.
It's not essential that you master the mathematics, though you should try your best to follow along as they do a really good job of explaining what some of the stickier bits of notation represent.
(Don't let the corny music at the start dissuade you, they're really good videos)