Machine Learning Problem Bible (MLPB) is a collection of machine learning problems and solutions. It attempts to solve a few problems.
– One of the best ways to start solving a problem is to analyze the solution to a similar problem. MLPB is an organized collection of machine learning problems and solutions, each with tags like “regression”, “natural-language-processing”, “hierarchical-data”, “random-forest”, etc. making it easy to identify problems and solutions related to the one at hand.
– MLPB contains example solutions using the same model (e.g. gradient boosting) from different implementations (e.g. Python’s scikit-learn package, or XGBoost). This makes it easy to compare benefits and drawbacks of different programming languages and packages – not just different models.
– All the examples in MLPB contain small datasets (thousands of rows or less), keeping the focus on understanding algorithms. This makes it easy to check intuitions about a model’s behavior and performance.