Machine Learning Overview (page 11 of 13)

One of the objectives of the model training process is to balance bias and variance. In this context, bias is the difference between expected prediction from the model and the correct values. Variance is how the predictions from the model differ between training and testing. The ideal model is low bias and low variance, but this is typically not realistic for many complex business scenarios. Reducing the bias in our model often results in greater variance (vice versa).