Classification and

regression tree(or decision tree) is broadly usedmachine learningmethod for modeling. They are favorite because of these factors:

- simple to understand (white box)
- from a tree we can extract interpretable results and make simple decisions
- they are helpful for exploratory analysis as binary structure of tree is simple to visualize
- very good prediction accuracy performance
- very fast
- they can be simply tuned by ensemble learning techniques
But! There is always some “but”, they poorly adapt when new unexpected situations (values) appears. In other words, they can not detect and adapt to change or concept drift well (absolutely not). This is due to the fact that tree creates during learning just simple rules based on training data. Simple decision tree does not compute any regression coefficients like linear regression, so trend modeling is not possible. You would ask now, so why we are talking about time series forecasting with regression tree together, right? I will explain how to deal with it in more detail further in this post.

This was a very interesting article. Absolutely worth reading. H/T R-Bloggers