Bias and Variance Cheat Sheet

  • Both are Machine Learning terms
  • High Bias
    • Also known as underfitting
    • Your model cannot learn from features
    • Bias means your model is too simple and keen on its own understanding and fail to learn new things
  • High Variance
    • Also known as overfitting
    • You model is too complex, it is specific to training data
    • Variance means your model cares about too much details, thus unable to generalize
  • Fighting Bias
    • Make your model more complex
  • Fighting Variance
    • Add regulation so your model will be less affected by outlier features
    • Make your model simpler thus more general

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