add doc for Python wrapper
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@ -124,3 +124,8 @@ data = np.random.rand(7,10) # 7 entities, each contains 10 features
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dtest = xgb.DMatrix( data, missing = -999.0 )
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ypred = bst.predict( xgmat )
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```
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If early stopping is enabled during training, you can predict with the best iteration.
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```python
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ypred = bst.predict(xgmat,ntree_limit=bst.best_iteration)
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```
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