We use the machine learning/data science packages such as numpy, sklearn, pandas, and matplotlib for visualization:

from time import time
 import numpy as np
 import matplotlib.pyplot as plt
 import pandas as pd
 from sklearn.model_selection import cross_val_score

To implement the isolation forest, we use the sklearn.ensemble package:

from sklearn.ensemble import IsolationForest

To measure the performance, we use the ROC and AUC, and we will discuss these in details in a later part of this chapter.

The following code imports the relevant packages and loads the KDD data:

 from sklearn.metrics import roc_curve, auc
 from sklearn.datasets import fetch_kddcup99
 %matplotlib inline

dataset = fetch_kddcup99(subset=None, shuffle=True, percent10=True)
 # http://www.kdd.org/kdd-cup/view/kdd-cup-1999/Tasks
 X = dataset.data
 y = dataset.target