can you tell me the next step in this exemple because I don't know how should I continu?? please

Dishant Pal did you find an answer elsewhere? would like to know as well. Perhaps , in the case of numerical values as predictors, it will split on boolean statement like if X < Y. for example, if height > 2m athlete plays basketball, else athlete plays other sport.

because this is Regression not a classification problem If we try to apply Entropy to regression problems, we run into the following problems: 1. Entropy does not use any information about the relative closeness of one value to another in calculating how good a split is. Therefore, if one split resulted in “a”s and “b”s being isolated, and another resulted in “a”s and “z”s, then the split quality would be the same. Intuitively, if the first were “1”s and “2”s, and the latter were “1”s and “100”s, then we should conclude that the first split was a better partition than the second. Therefore, for regression problems, we need a way of taking into account how close values. 2. If the target space is continuous then Entropy will treat each continuous target value as a distinct category (eg. 12.5 and 0.432), one for every value! www.appliedaisystems.com/papers/RegressionTrees.doc Reference : Regression Trees Brendan Kitts Read this paper If you are using sklearn you can see implimentation here github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_criterion.pyx cdef class RegressionCriterion(Criterion): r"""Abstract regression criterion. This handles cases where the target is a continuous value, and is evaluated by computing the variance of the target values left and right of the split point. The computation takes linear time with `n_samples` by using :: var = \sum_i^n (y_i - y_bar) ** 2 = (\sum_i^n y_i ** 2) - n_samples * y_bar ** 2 """