4
votes
Accepted
Make a random forest estimator the exact same of a decision tree
You need to set bootstrap=False in the random forest to disable the subsampling. (I originally commented because I expected there to be more impediments [in ...
- 12k
3
votes
List of samples that each tree in a random forest is trained on in Scikit-Learn
I don't think it is possible to get it directly but we may utilize the random seed.
random_stateint, RandomState instance or None, default=None
Controls both the randomness of the bootstrapping of ...
- 5,654
2
votes
Accepted
scikit learn target variable reversed (DecisionTreeClassifier)
It might be because of the conflict of order between model.classes_ and series.unique()
For a binary labels,
...
- 5,654
2
votes
N-ary decision tree with categorical features
It looks like your features are not really categorical, at least age: with categorical features the possible values are known at training, so normally you cannot ...
- 25.7k
1
vote
XGB predict_proba estimates don't match sum of leaves
When you call predict_proba in XGBoost, it returns the probability estimates calculated by averaging the predictions of all the ...
- 3,009
1
vote
Accepted
How do the splits points in a decision tree within Random Forest are taken/selected? (Base on which criteria?)
You're asking multiple questions, so I will try to answer them all, and then give a piece of code that I used for exploration.
First, here is a summary of how a RandomForestClassifier works:
it ...
- 1,425
1
vote
Simple CART model example
In the sklearn docs it is stated that:
scikit-learn uses an optimised version of the CART algorithm; however,
scikit-learn implementation does not support ...
- 7,596
1
vote
List of samples that each tree in a random forest is trained on in Scikit-Learn
It is possible, actually. The answer is not too different than the one given by @10xAI, but it is not trying to exploit the order of the random seeds implicitly, since it would break for parallel ...
- 111
1
vote
how are split decisions for observations(not features) made in decision trees
Do i also need to try all the combinations of a feature split, for above case, height < 100 & height > 100, then height < & height > 110 & height < 90 & height > 90 ...
- 6,332
1
vote
Random selection of variables in each run of python sklearn decision tree (regressio )
The documentation says:
random_state : int, RandomState instance, default=None
Controls the randomness of the estimator. The features are always randomly permuted at each split, even if splitter is ...
- 25.7k
1
vote
Problems with decision tree labeling of nodes
Decision Tree does assign the label based on majority given the attribute test condition and its value.
Regarding the class label assignment-
In case DT has a longer depth, there might not be ...
- 698
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