Gentle Introduction to the Bias-Variance Trade-Off in Machine Learning

The prediction error for any machine learning algorithm can be broken down into three parts:

  1. Bias Error
  2. Variance Error
  3. Irreducible Error

The irreducible error cannot be reduced regardless of what algorithm is used. It is the error introduced from the chosen framing of the problem and may be caused by factors like unknown variables that influence the mapping of the input variables to the output variable.

Understanding the difference
The optimal balance of course is where bias and variance are at their minimum

Find out more here:

The Bias-Variance Trade-Off in Machine Learning


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