changed intro to trees

2019-12-26 10:00:10 +01:00
parent 0577805564
commit f170a1a9d5
10 changed files with 56 additions and 6 deletions
@@ -7,6 +7,13 @@ DATE: today
 ===== Decision trees, overarching aims  =====


+We start here with the most basic algorithm, the so-called decision
+tree. With this basic algorithm we can in turn build more complex
+networks, spanning from homogeneous and heterogenous forests (bagging,
+random forests and more) to one of the most popular supervised
+algorithms nowadays, the extreme gradient boosting, or just
+XGBoost. But let us start with the simplest possible ingredient.
+
 Decision trees are supervised learning algorithms used for both,
 classification and regression tasks.

@@ -22,6 +29,8 @@ to be the most informative ones. The process of finding the _most
 informative_ feature is done until we accomplish a stopping criteria
 where we then finally end up in so called _leaf nodes_. 

+
+
 A decision tree is typically divided into a _root node_, the _interior nodes_,
 and the final _leaf nodes_ or just _leaves_. These entities are then connected by so-called _branches_.