April 2018
Beginner to intermediate
282 pages
6h 52m
English
In this section, you will write a wrapper function to optimize the XGBoost algorithm hyperparameters to improve performance on the Breast Cancer Wisconsin dataset:
# Importing necessary librariesimport numpy as npfrom xgboost import XGBClassifierfrom sklearn import datasetsfrom sklearn.model_selection import cross_val_score# Importing ConfigSpace and different types of parametersfrom smac.configspace import ConfigurationSpacefrom ConfigSpace.hyperparameters import CategoricalHyperparameter, \ UniformFloatHyperparameter, UniformIntegerHyperparameterfrom ConfigSpace.conditions import InCondition# Import SMAC-utilitiesfrom smac.tae.execute_func import ExecuteTAFuncDictfrom smac.scenario.scenario import Scenariofrom smac.facade.smac_facade ...