Preventing Overfitting During Model Training

Requirements Clarification & Assessment

Before diving into solutions, it's essential to clearly understand the problem of overfitting and the specific context in which it occurs:

1. Understanding Overfitting:

   • Overfitting happens when a model learns the noise and details in the training data to the extent that it negatively impacts the model's performance on new data.
   • It results in a model that performs well on training data but poorly on unseen data.

2. Identifying Causes:

   • Data-related Issues: Small dataset size, noisy data, or irrelevant features.
   • Model Complexity: Overly complex models with too many parameters.

3. Assessing the Context:

   • What type of model or algorithm is being used?
   • What is the size and nature of the dataset?
   • Are there specific constraints or requirements (e.g., computational resources, time, etc.)?

4. Defining the Goal:

   • Aim to develop a model that generalizes well to unseen data while maintaining acceptable performance on the training set.
   • Ensure the model is robust, scalable, and interpretable where necessary.