In machine learning (ML), bias is a concept which is related to errors in the model's predictions, as a results of multiple assumptions and simplifications in the machine learning algorithm. Due to these assumptions, the ML model becomes easy to explain (explainability) but it often misses to capture the complexity inside the training and testing ... Read more
A "good fit" or "best fit" or "sweet spot" is when a machine learning (ML) model can predict values for a system with the minimum error, ideally that error being zero. In this case, the ML model is said to have a good fit on the data. The good fit sits between the underfitting and ... Read more
In machine learning (ML), variance is a concept which is related to errors in the model's predictions, as a results of over-sensitivity and high correlation of the machine learning algorithm to the training data. Due to this over-sensitivity, the ML model becomes complex to explain (explainability) and it captures the complexity inside the training data ... Read more
