Figure 5.

Model fitting errors. Variance is the variability (distance from the target center) of the model prediction for a single point. Bias is the distance between expected (target center) and means values. In overfitting, the model has high Variance and low Bias. It shows high performance on the training set but not on the test one. Underfitting is characterized by high Bias and low Variance and produces a poor performance on the training set.