HB_ML_Challenge/xgboostR/wrapper/xgboost.py
def predict(self, data, output_margin=False):
        """
        predict with data
            data: the dmatrix storing the input
            output_margin: whether output raw margin value that is untransformed
        """
        length = ctypes.c_ulong()
        preds = xglib.XGBoosterPredict(self.handle, data.handle,
                                       int(output_margin), ctypes.byref(length))
        return ctypes2numpy(preds, length.value, 'float32')

        predict with data
            data: the dmatrix storing the input
            output_margin: whether output raw margin value that is untransformed
        
predict with data
data: the dmatrix storing the input
output_margin: whether output raw margin value that is untransformed
predict with data data : the dmatrix storing the input output_margin : whether output raw margin value that is untransformed
def predict(self, data, output_margin=False):
        length = ctypes.c_ulong()
        preds = xglib.XGBoosterPredict(self.handle, data.handle,
                                       int(output_margin), ctypes.byref(length))
        return ctypes2numpy(preds, length.value, 'float32')
def predict ( self , data , output_margin = False ) : length = ctypes . c_ulong ( ) preds = xglib . XGBoosterPredict ( self . handle , data . handle , int ( output_margin ) , ctypes . byref ( length ) ) return ctypes2numpy ( preds , length . value , 'float32' )
predict with data
data: the dmatrix storing the input
output_margin: whether output raw margin value that is untransformed
predict with data data : the dmatrix storing the input output_margin : whether output raw margin value that is untransformed
"""
        predict with data
            data: the dmatrix storing the input
            output_margin: whether output raw margin value that is untransformed
        """
self
data
output_margin