Playground for Feature Distribution in Machine Learning
During the Extract, Transform, Load (ETL) process, data analysts perform various tasks such as data cleaning, data transformation, and data visualization. One of the key responsibilities is to gain a deep understanding of the data and its underlying features. Analyzing the distribution of these features is crucial for data analysts and scientists.
To explore the impact of varying distributions on machine learning models, I have created a simple playground. This playground allows users to experiment with different distributions and observe how they affect the performance of the machine learning model. The playground focuses on a single feature and two distinct classes. In essence, it can be thought of as a simplified IF…then…ELSE scenario, although there are situations where this translation may not hold true.
Feel free to explore the playground and contribute to its source code.
