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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 several methods for dealing with imbalanced data:

  1. Begin by loading the training and testing data, importing a decision tree, as well as some libraries we will be using to score performance:
from sklearn import treefrom sklearn.metrics import balanced_accuracy_scoreimport numpy as npimport scipy.sparseimport collectionsX_train = scipy.sparse.load_npz("training_data.npz")y_train = np.load("training_labels.npy")X_test = scipy.sparse.load_npz("test_data.npz")y_test = np.load("test_labels.npy")
  1. Train and test a simple Decision Tree classifier:
dt = tree.DecisionTreeClassifier()dt.fit(X_train, y_train)dt_pred = dt.predict(X_test)print(collections.Counter(dt_pred))print(balanced_accuracy_score(y_test, ...
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Publisher Resources

ISBN: 9781789614671Supplemental Content