Intel® Optimization for XGBoost Cheat Sheet

Basic Installation Using PyPI*

pip install xgboost

Basic Installation Using Anaconda*

сonda install xgboost -c conda-forge

Enable Training Optimizations from Intel Using the hist Tree Method Parameter as an Example

# Note that tree_method is set to hist
 

import xgboost as xgb

import numpy as np

# Set XGBoost parameters

xgb_params = {

   'alpha':            0.9,

   'max_bin':          256,

   'scale_pos_weight': 2,

   'learning_rate':    0.1,

   'subsample':        1,

   'reg_lambda':       1,

   'min_child_weight': 0,

   'max_depth':        8,

   'max_leaves':       2**8,

   'tree_method':       hist

}

# Create DMatrix from the training set (in CSV format)

dtrain = xgb.DMatrix('letters_train.csv')

# Train the model