Addressing Overfitting in Models

Requirements Clarification & Assessment

1. Understanding Overfitting:

   • Definition: Overfitting occurs when a model captures noise and random fluctuations in the training data rather than the intended outputs. This results in poor generalization to new, unseen data.
   • Symptoms: High accuracy on training data but poor performance on validation/testing data.

2. Contextual Factors:

   • Data Size: Is the dataset large enough to support the complexity of the model?
   • Model Complexity: Is the model overly complex for the given data?
   • Feature Selection: Are there irrelevant or redundant features contributing to noise?
   • Training Process: Are there signs of overtraining, such as excessive epochs in neural networks?

3. Objective:

   • Goal: Develop a model that generalizes well across unseen data while maintaining interpretability and efficiency.
   • Constraints: Time, computational resources, and availability of additional data.