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Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

An example system

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 ...
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Publisher Resources

ISBN: 9781788629898Supplemental Content