Uncovering Anomalies with Copula-Based Outlier Detection

In this article, we dive into the world of machine learning and explore how categorical features are encoded in data. We learn that all eight categorical features in a dataset are encoded using Catboost encoding, which is a technique that assigns a numerical value to each category. This is important because it allows machine learning algorithms to analyze and make predictions based on these categories.
Next, we discuss the issue of class imbalance in datasets. Imagine you have a recipe book with different ingredients, but some of them are much more common than others. In this case, the algorithm might struggle to recognize the rare ingredient when it appears in a dish. Similarly, in machine learning, if one class is much more common than another, the algorithm might have trouble accurately predicting the minority class.
To address this challenge, we look at self-supervised methods that use pretext tasks for anomaly detection. These tasks are like training a chef to recognize unusual ingredients by exposing them to different dishes. By doing so, the chef can learn to identify and handle unexpected combinations of ingredients better.
In summary, this article provides an overview of categorical features, encoding, and imbalanced datasets in machine learning. It also discusses self-supervised methods for anomaly detection and how they help overcome class imbalance issues. By understanding these concepts, we can better appreciate the complexity of machine learning algorithms and their ability to handle diverse data types with ease.