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I would call this bad feature engineering, I'm afraid: as the designer of a ML system, one is supposed to analyze their data and find the best way to make the ML system perform as well as possible. In this case by adding a simple feature x % 2 for every instance the decision tree can perform perfectly. [added] Even in the case of a more complex pattern, if ...


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It's not true in general. Decision trees tends to overfit in comparison to other algorithms, which provide too low accuracy. But if you use a decision tree in the right way i.e you prepare data in the proper format, use feature selection and perform k-fold cross-validation everything should be ok. I am sure that you misread it. There is no reason why DT ...


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Assuming I can’t use external datasets, what’s the solution to this problem? How can I best understand those who have defaulted if I don’t have something to compare them to? If you wanna use this only this data as classification task, you can't perform this task. You could way this around, by generated fake data with label 0(you think about which value of ...


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Good job looking at the tree and understanding what has happened. There is no problem splitting on the same feature multiple times. A continuous feature has many split points available. The tree continues to subset and refine. The split criteria shows what will be the "best" greedy split at this point. If a feature is income, perhaps the best split is \$100,...


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The underlying model will be a stepwise function. I don't see any garanty that it will work better (or worse) with the transformation, in the general case. This may be different depending on you variable (for binary variables you may want to work directly on the linear predictor). However, in practice, if you know there is an underlying link transformation,...


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