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Data Interview Question

Preventing Overfitting During Model Training

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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.