Practical Exercises in Novelty and Outlier Detection
In this final section, we’ll engage in practical exercises that involve detecting, evaluating, and handling anomalies in real-world datasets. These exercises are designed to reinforce the concepts introduced throughout the chapter—ranging from model selection to evaluation and strategy implementation. By the end of this section, you’ll have direct experience working with a variety of detection methods and be better equipped to select and fine-tune them based on your data and goals.
Exercise 1: Applying Isolation Forest to a Real-World Dataset
In this exercise, we simulate a real-world anomaly detection scenario using synthetic data. The dataset consists of a dense cluster of normally distributed points and a smaller group of uniformly distributed outliers. You'll scale the data, apply the Isolation Forest algorithm, generate predictions, and evaluate the model's ability to identify anomalies.
Implementation...
