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
