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Machine Learning for Cybersecurity Cookbook
book

Machine Learning for Cybersecurity Cookbook

by Emmanuel Tsukerman
November 2019
Intermediate to advanced content levelIntermediate to advanced
346 pages
9h 36m
English
Packt Publishing
Content preview from Machine Learning for Cybersecurity Cookbook

How to do it...

In the following steps, you will load the standard wine dataset and use Bayesian optimization to tune the hyperparameters of an XGBoost model:

  1. Load the wine dataset from scikit-learn:
from sklearn import datasetswine_dataset = datasets.load_wine()X = wine_dataset.datay = wine_dataset.target
  1. Import XGBoost and stratified K-fold:
import xgboost as xgbfrom sklearn.model_selection import StratifiedKFold
  1.  Import BayesSearchCV from scikit-optimize and specify the number of parameter settings to test:
from skopt import BayesSearchCVn_iterations = 50
  1. Specify your estimator. In this case, we select XGBoost and set it to be able to perform multi-class classification:
estimator = xgb.XGBClassifier(    n_jobs=-1, objective="multi:softmax", ...
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Publisher Resources

ISBN: 9781789614671Supplemental Content