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Bhavesh Badani

Bhavesh Badani

01-May-2024
  • Overfitting: Generally Occurs when a model learns the training data too well or too deep in a menner such that it also capture noise and specific details that don’t generalize to new data. Signs of overfitting include high training accuracy but poor performance on unseen data.
  • Ways to prevent overfitting:
    • Regularization: Directly Introduce penalties for complex models (e.g., L1 or L2 regularization), which may regulate overfitting.
    • Early Stopping: Monitor validation performance and stop training as soon as it starts to degrade.
    • Feature Selection: Choose relevant features and avoid unnecessary complexity. This is very helpful step when it comes to prevent overfitting.
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