ML Models Using Identical Datasets

Requirements Clarification & Assessment

1. Objective of Model Evaluation

   • The primary goal of evaluating a machine learning model is to understand its predictive capabilities on unseen data. This requires a clear distinction between training data (used for learning) and testing data (used for evaluation).

2. Understanding Overfitting

   • Overfitting occurs when a model learns the noise and patterns of the training data to an extent that it performs poorly on new, unseen data. It's crucial to identify overfitting during model evaluation.

3. Concept Drift Consideration

   • Concept drift refers to the change in data patterns over time. A robust evaluation should consider whether the model can adapt to such changes, which requires diverse and time-varied testing data.

4. Dataset Partitioning

   • Proper partitioning of datasets into training, validation, and testing sets is essential. This ensures the model's evaluation accurately reflects its ability to generalize to novel situations.

5. Real-World Application

   • The evaluation process should mimic real-world scenarios as closely as possible, ensuring that the model's performance metrics are representative of its use in practical applications.