Currently i am using random forest of sklearn to test my idea.
But as we know, sklearn only works in CPU.
So i am trying to replace it to random forest of XGBoost to use GPU
But i am still not clear to understand somthing. So i want to ask.
To use random forest with mini-batch in sklearn i have to initialize random forest with
warm_start=True and increase
n_estimator when train it.
RFT = RandomForestClassifier(n_estimators=100, random_state=1, n_jobs=-1, warm_start=True) RFT.fit(mini_batch_data, mini_batch_label) RFT.n_estimators += 1
But in random forest of xgboost
There is no
warm_start parameter in api reference
And in example of regressor(it might be different from classifier)
in document of
random forest in xgboost , XGBRFRegressor seems like using mini batch training right…?
This is example
from sklearn.model_selection import KFold # Your code ... kf = KFold(n_splits=2) for train_index, test_index in kf.split(X, y): xgb_model = xgb.XGBRFRegressor(random_state=42).fit( X[train_index], y[train_index])
So i am curious that random forest of xgboost use mini batch train automatically? or is there no mini batch training?
Thank you for reading!