c024c42dce
* Use sphinx gallery to integrate examples. * Remove mock objects. * Add dask doc inventory.
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
"""
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Demo for boosting from prediction
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=================================
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"""
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import os
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import xgboost as xgb
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CURRENT_DIR = os.path.dirname(__file__)
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dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
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dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.test'))
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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###
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# advanced: start from a initial base prediction
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#
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print('start running example to start from a initial prediction')
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# specify parameters via map, definition are same as c++ version
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param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
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# train xgboost for 1 round
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bst = xgb.train(param, dtrain, 1, watchlist)
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# Note: we need the margin value instead of transformed prediction in
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# set_base_margin
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# do predict with output_margin=True, will always give you margin values
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# before logistic transformation
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ptrain = bst.predict(dtrain, output_margin=True)
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ptest = bst.predict(dtest, output_margin=True)
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dtrain.set_base_margin(ptrain)
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dtest.set_base_margin(ptest)
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print('this is result of running from initial prediction')
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bst = xgb.train(param, dtrain, 1, watchlist)
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