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Is it possible to 'group features' for a decision tree model?

This can be achieved, and it is already implemented in XGBoost. See a full description here.
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Show how to obtain decision tree will classify the test instance <sunny,mild,normal,weak>?

It is not necessary to match all features. If outlook is Sunny and humidity is normal the answer will be yes irrespective what is the value for wind, temperature . In other words , if the outlook is ...
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What is the implication of having features with less variation in a tree based model?

Variation does not matter what matters is how good is your variable in predicting the target variable. in order to know whether linear model is applicable, you have to fit data on linear ...
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How to use "tree boosting" with a data-driven loss function

As for me: I don’t see any problem except for the simple logics of XGBoosting: With weak learner – you’re getting 1st derivative (gradient). Further with strong lerner – you’re getting 2nd derivative (...
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The best Python library to build decision tree on binary inputs

Why don't you start with sklearn. You can set maximum number of elements in leaf Minimum element at root Decision Tree takes batch algorithm. You can input 20k samples all at once. The CPU config ...
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What is the implication of having features with less variation in a tree based model?

Variation is not the key. Notice that 0/1 indicator variables are used frequently and might have mostly 0's (like many missing indicators). The key is where is the variation in relation to what you ...
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How can we shorten our questionnaire to only ask the most informative question at each point?

I'm not at all sure this is the ideal solution, but it is an easy one: just use a regression tree (e.g. DecisionTreeRegressor from sklearn), training it with both ...
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