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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, we will demonstrate how to instantiate, train, and test an XGBoost classifier:

  1. Start by reading in the data:
import pandas as pddf = pd.read_csv("file_pe_headers.csv", sep=",")y = df["Malware"]X = df.drop(["Name", "Malware"], axis=1).to_numpy()
  1. Next, train-test-split a dataset:
from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
  1. Create one instance of an XGBoost model and train it on the training set:
from xgboost import XGBClassifierXGB_model_instance = XGBClassifier()XGB_model_instance.fit(X_train, y_train)
  1. Finally, assess its performance on the testing set:
from sklearn.metrics import accuracy_scorey_test_pred = XGB_model_instance.predict(X_test) ...
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Publisher Resources

ISBN: 9781789614671Supplemental Content