Anomaly detection is crucial in data mining and machine learning, finding applications in fraud detection, network security, and more. The Isolation Forest algorithm, introduced by Fei Tony Liu and Zhi-Hua Zhou in 2008, stands out among anomaly detection methods. It uses decision trees to efficiently isolate anomalies by randomly selecting features and splitting data based on threshold values. This approach is effective in quickly identifying outliers, making it well-suited for large datasets where anomalies are rare and distinct.
As in this article, we delve into the workings of the Isolation Forest algorithm, its implementation in Python, and its role as a powerful tool in anomaly detection. We also explore the metrics used to evaluate its performance and discuss its applications across various domains.
Also, you will learn how to implement the Isolation Forest model in Python for outlier detection. This powerful technique is widely used for anomaly detection, providing robust results in various datasets.
This article was published as a part of the Data Science Blogathon.
Isolation Forest is a method used to find unusual data points, known as anomalies or outliers, in a dataset. It is particularly good at spotting these anomalies in large amounts of data.
Since its introduction, Isolation Forest has gained popularity as a fast and reliable algorithm for anomaly detection in various fields such as cybersecurity, finance, and medical research.
Isolation Forests(IF), similar to Random Forests, are build based on decision trees. And since there are no pre-defined labels here, it is an unsupervised model.
Isolation Forests were built based on the fact that anomalies are the data points that are “few and different”.
In an Isolation Forest, randomly sub-sampled data is processed in a tree structure based on randomly selected features. The samples that travel deeper into the tree are less likely to be anomalies as they required more cuts to isolate them. Similarly, the samples which end up in shorter branches indicate anomalies as it was easier for the tree to separate them from other observations.
Let’s take a deeper look at how this actually works.
As mentioned earlier, Isolation Forests outlier detection are nothing but an ensemble of binary decision trees. And each tree in an Isolation Forest is called an Isolation Tree(iTree). The algorithm starts with the training of the data, by generating Isolation Trees.
Let us look at the complete algorithm step by step:
After creating an ensemble of iTrees (Isolation Forest), the model training is complete. During scoring, the system traverses a data point through all the trees that were trained earlier. Now, an ‘anomaly score’ is assigned to each of the data points based on the depth of the tree required to arrive at that point. This score is an aggregation of the depth obtained from each of the iTrees. An anomaly score of -1 assigns anomalies and 1 to normal points based on the contamination parameter (percentage of anomalies present in the data).
Source: IEEE. We can see that it was easier to isolate an anomaly compared to a normal observation.
Let us look at how to implement Isolation Forest in Python.
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.ensemble import IsolationForest
data = pd.read_csv('marks.csv')
data.head(10)
Output:
import pandas as pd
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
data = pd.read_csv('marks.csv')
print(data.head(10))
sns.boxplot(data.marks)
plt.show()
From the box plot, we can infer that there are anomalies on the right.
random_state = np.random.RandomState(42)
model=IsolationForest(n_estimators=100,max_samples='auto',contamination=float(0.2),random_state=random_state)
model.fit(data[['marks']])
print(model.get_params())
Output:
{'bootstrap': False, 'contamination': 0.2, 'max_features': 1.0, 'max_samples': 'auto', 'n_estimators': 100, 'n_jobs': None, 'random_state': RandomState(MT19937) at 0x7F08CEA68940, 'verbose': 0, 'warm_start': False}
You can take a look at Isolation Forest documentation in sklearn to understand the model parameters.
data['scores'] = model.decision_function(data[['marks']])
data['anomaly_score'] = model.predict(data[['marks']])
data[data['anomaly_score']==-1].head()
Output:
Here, we can observe that both anomalies are assigned an anomaly score of -1.
accuracy = 100*list(data['anomaly_score']).count(-1)/(anomaly_count)
print("Accuracy of the model:", accuracy)
Output:
Accuracy of the model: 100.0
What happens if we change the contamination parameter? Give it a try!!
Isolation Forests are computationally efficient and researchers have proven them to be very effective in anomaly detection. Despite its advantages, there are a few limitations as mentioned below.
Well, to understand the second point, we can take a look at the below anomaly score map.
Source: IEEE
Here, in the score map on the right, we can observe that the points in the center obtained the lowest anomaly score, as expected. However, we can see four rectangular regions around the circle with lower anomaly scores as well. So, when scoring a new data point in any of these rectangular regions, it might not detect it as an anomaly.
Similarly, in the above figure, we can see that the model resulted in two additional blobs(on the top right and bottom left ) which never even existed in the data.
Whenever a node splits in an iTree based on a threshold value, it divides the data into left and right branches, resulting in horizontal and vertical branch cuts. And these branch cuts result in this model bias.
The above figure shows branch cuts after combining outputs of all the trees of an Isolation Forest. Here, we can observe how the rectangular regions with lower anomaly scores formed in the left figure. And also the right figure shows the formation of two additional blobs due to more branch cuts.
To overcome this limit, an extension to Isolation Forests called ‘Extended Isolation Forests’ was introduced by Sahand Hariri. In EIF, horizontal and vertical cuts were replaced with cuts with random slopes.
Despite introducing EIF (Extended Isolation Forest), the use of Isolation Forests for anomaly detection remains widespread across various fields.
The Local Outlier Factor (LOF) algorithm is a powerful tool for detecting anomalies in data by evaluating the density of points relative to their neighbors. Unlike traditional anomaly detection methods, LOF does not require assuming a specific data distribution, making it suitable for a wide range of applications. When implementing LOF, setting the n_estimators parameter appropriately ensures robust performance, balancing computational efficiency with detection accuracy. It is important to note that LOF performs well with both normal and anomalous instances, as it identifies points that deviate significantly from their local neighborhood.
Hope you like the isolation forest example, which demonstrates the isolation forest anomaly detection algorithm in Python. The isolation forest is a powerful tool for identifying anomalies in complex datasets. By applying the isolation forest anomaly detection method, you can isolate outliers and gain valuable insights from your data. The isolation forest algorithm is a highly effective solution for various applications that require anomaly detection.
By leveraging random partitioning and regression techniques, LOF achieves scalable and efficient anomaly detection, even with large datasets. Adjusting the sampling size parameter enables fine-tuning of the algorithm to suit specific data characteristics, ensuring optimal results in real-world applications.
A. Random Forest is a supervised learning algorithm for classification and regression tasks using decision trees. Isolation Forest is an unsupervised algorithm for anomaly detection, isolating anomalies based on unique properties.
A. Isolation Forest works by randomly selecting a feature from the dataset and a split value to create partitions of the data. The process repeats recursively until it isolates anomalies in their own partitions.
Isolation Forest quickly finds outliers by randomly splitting data. Works well on large datasets but needs the right number of expected outliers.
Isolation Forest doesn’t measure distance. It finds outliers by seeing how quickly it can isolate data points. Points that are easy to isolate are likely outliers.
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