to build treethe algorithm searches over all possible tests and finds the one that is most informative about the target variable figure - shows the first test that is picked splitting the dataset vertically at [ ]= yields the most informationit best separates the points in class from the points in class the top nodealso called the rootrepresents the whole datasetconsisting of points belonging to class and points belonging to class the split is done by testing whether [ < indicated by black line if the test is truea point is assigned to the left nodewhich contains points belonging to class and points belonging to class otherwise the point is assigned to the right nodewhich contains points belonging to class and points belonging to class these two nodes correspond to the top and bottom regions shown in figure - even though the first split did good job of separating the two classesthe bottom region still contains points belonging to class and the top region still contains points belonging to class we can build more accurate model by repeating the process of looking for the best test in both regions figure - shows that the most informative next split for the left and the right region is based on [ supervised learning