I'm doing a project. I have a classification problem that I should solve using gradient boosted decision trees. What I want to do is create a matrix that gives prediction of each decision tree for each sample. For example if I have 100 samples and 100 trees, I should have 100x100 matrix. i, j th entry gives the prediction of jth tree for ith sample.
I'm using sklearn and problem is I can't get prediction by each tree.
So far I tried:
newgb=gb.estimators_.fit(X_train, y_train) print(newgb.score(X_train, y_train))
where gb is already a fitted model. What I understood from documentation of sklearn
should return (number-of-trees x 1) matrix, each entry contains a tree that used by our model. By gb.estimators_ I tried to access to the first tree, and predict it with score. What I get as output is:
[0.12048193 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.12048193 0.95 0.95 0.95 0.12048193 0.12048193 0.12048193 0.12048193 ...]
None of them are 1 or 0, like it should be(it is binary classification) and values repeat themselves like 0.95 and 0.12. I didn't use any likelihood function either so
supposed to give me only 1's and 0's.
I don't know how to get predictions for each individual tree. I don't know what I do wrong either.