By Jordi de la Torre on September 22, 2015
Sometimes prediction error sources are not well understood.
These two important concepts are easily understood after visualizing them:
A low bias - high variance model would give predictions where its expected average is near the true value, but its individual predictions are highly dispersed around the true value.
A high bias - low variance model would give predictions far from the true value but with small variability between the predictions.
A high bias - high variance model would give predictions far from the true value and with high variability between them.
Finally a low bias - low variance model would be the best one having close to the true value predictions and with low variability between individual predictions.