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1

Same as with regular decision tree, isolation forest is not trained by directly minimizing some loss, but by using a dedicated algorithm. If you are interested in the algorithm, check the paper by it's authors, where they describe it in detail: Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. (2008). “Isolation forest.” Data Mining. ICDM’08.


6

The reason that the Decision Tree does poorly here is the algorithm isn't equipped to deal with the situation you're throwing at it. You need to understand how a CART model gives its predicted output value for a continuous response. You fit a CART model to the response target, predicted by inputs category and A. You want the decision tree to learn the rule ...


3

This is not how decision trees work. Roughly speaking, decision tree splits data into bins (branches), conditionally on the features, and per each bin it predicts mean of the target variable. So for decision tree to predict something like identity function $y = f(y)$, you would need decision tree with the number of branches equal to the size of the data, i.e....


2

Based on the example added to the original question, it seems that you already have data on the "popularity" of the articles. In that case I agree with @ttnphns that the best approach for discretization, if you really have to do it, would be to break up the known continuous "popularity" measure into 4 equal sized groups representing ...


0

The term "classification" is generally used to refer to situations where the output is from a small number of options that do not have any structure, such as an inherent order (the term is sometimes still used in cases where the output does have a structure, but usually it doesn't). The definition of "regression" is often given as meaning ...


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Consider linear regression with a single parameter. It will always predict one number, namely the sample mean of the outcome on which it was trained. Is this regression even though it can only give one output? If you subscribe to the definition of regression as a learning problem in which the output is quantitative rather than categorical (or perhaps more ...


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