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- 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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