# Prediction error: bias vs variance

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:

**Bias** is the difference between the expected average prediction and the real value.

**Variance** measures the variability of the model prediction for a given data point.

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.