Anomaly detection helps find unusual data points that stand out from the rest. In unsupervised learning, two popular methods for this are Isolation Forests and Autoencoders. Let’s look at how they work and what they are best for.
Isolation Forests use a special method that involves trees. The main idea is "isolation."
Random Sampling: Isolation Forests create many decision trees by randomly picking parts of the data. This helps break the data into smaller pieces.
Path Length: Anomalies usually have shorter paths in this tree setup. This means they can be found more easily, as they are different from most of the data. If it takes fewer cuts to isolate a data point, it might be an anomaly.
Scoring: Each data point gets a score based on how long its path is in all the trees. A short score means it could be an anomaly, while a long score suggests it’s more normal.
Example: Think about customer transactions. An Isolation Forest could spot fraudulent transactions because they would be isolated in a sparse area of the data.
On the other hand, Autoencoders are a type of neural network. They learn to make a smaller version of the data.
Architecture: An Autoencoder has two parts: an encoder that shrinks the data and a decoder that rebuilds it back to normal.
Reconstruction Error: The goal is to minimize the difference between what goes in and what comes out. After training, an Autoencoder can rebuild normal data well, but it will have a hard time with unusual data, resulting in a bigger error.
Thresholding: To find anomalies, we set a limit for this error. If the error goes above this limit, we label the data point as an anomaly.
Example: In a network, Autoencoders can spot strange patterns in the traffic. Normal traffic has low reconstruction errors, while an attack or unusual activity creates a much higher error.
In summary, both Isolation Forests and Autoencoders are good at finding anomalies, but they work in different ways.
Isolation Forests use tree structures and focus on how easily a data point can be isolated, making them great for data where anomalies are clearly separate.
Autoencoders focus on recreating the data and checking errors, which is helpful for complex data where unusual points might still look similar to normal ones but have different patterns.
Choosing which method to use depends on the specific data and the type of anomalies you want to find.
Anomaly detection helps find unusual data points that stand out from the rest. In unsupervised learning, two popular methods for this are Isolation Forests and Autoencoders. Let’s look at how they work and what they are best for.
Isolation Forests use a special method that involves trees. The main idea is "isolation."
Random Sampling: Isolation Forests create many decision trees by randomly picking parts of the data. This helps break the data into smaller pieces.
Path Length: Anomalies usually have shorter paths in this tree setup. This means they can be found more easily, as they are different from most of the data. If it takes fewer cuts to isolate a data point, it might be an anomaly.
Scoring: Each data point gets a score based on how long its path is in all the trees. A short score means it could be an anomaly, while a long score suggests it’s more normal.
Example: Think about customer transactions. An Isolation Forest could spot fraudulent transactions because they would be isolated in a sparse area of the data.
On the other hand, Autoencoders are a type of neural network. They learn to make a smaller version of the data.
Architecture: An Autoencoder has two parts: an encoder that shrinks the data and a decoder that rebuilds it back to normal.
Reconstruction Error: The goal is to minimize the difference between what goes in and what comes out. After training, an Autoencoder can rebuild normal data well, but it will have a hard time with unusual data, resulting in a bigger error.
Thresholding: To find anomalies, we set a limit for this error. If the error goes above this limit, we label the data point as an anomaly.
Example: In a network, Autoencoders can spot strange patterns in the traffic. Normal traffic has low reconstruction errors, while an attack or unusual activity creates a much higher error.
In summary, both Isolation Forests and Autoencoders are good at finding anomalies, but they work in different ways.
Isolation Forests use tree structures and focus on how easily a data point can be isolated, making them great for data where anomalies are clearly separate.
Autoencoders focus on recreating the data and checking errors, which is helpful for complex data where unusual points might still look similar to normal ones but have different patterns.
Choosing which method to use depends on the specific data and the type of anomalies you want to find.