Demo for boosting from prediction — xgboost 3.1.0-dev documentation (original) (raw)
Note
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import os
import xgboost as xgb
CURRENT_DIR = os.path.dirname(file) dtrain = xgb.DMatrix( os.path.join(CURRENT_DIR, "../data/agaricus.txt.train?format=libsvm") ) dtest = xgb.DMatrix( os.path.join(CURRENT_DIR, "../data/agaricus.txt.test?format=libsvm") ) watchlist = [(dtest, "eval"), (dtrain, "train")] ###
advanced: start from a initial base prediction
print("start running example to start from a initial prediction")
specify parameters via map, definition are same as c++ version
param = {"max_depth": 2, "eta": 1, "objective": "binary:logistic"}
train xgboost for 1 round
bst = xgb.train(param, dtrain, 1, watchlist)
Note: we need the margin value instead of transformed prediction in
set_base_margin
do predict with output_margin=True, will always give you margin values
before logistic transformation
ptrain = bst.predict(dtrain, output_margin=True) ptest = bst.predict(dtest, output_margin=True) dtrain.set_base_margin(ptrain) dtest.set_base_margin(ptest)
print("this is result of running from initial prediction") bst = xgb.train(param, dtrain, 1, watchlist)