Now let’s take a look at the different combinations of bias and variance in machine learning models and the results they provide.
Low bias, low variance: ideal model
A machine learning model with low bias and low variance is considered ideal but is not often the case in the machine learning practice, so we can speak of “reasonable bias” and “reasonable variance.”
Low bias, high variance: results in overfitting
This combination results in inconsistent predictions that are accurate on average. It occurs when a model has too many parameters and fits too closely to the training data.
High bias, low variance: results in underfitting
Predictions are consistent but inaccurate on average in this scenario. This happens when the model doesn’t learn well from the training data or has too few parameters, leading to underfitting issues.
High bias, high variance: results in inaccurate predictions
With both high bias and high variance, the predictions are both inconsistent and inaccurate on average.