What are variance and bias?

Bias is the measure of the expected value of the results that differs from the true underlying quantitative parameter being estimated.

Variance describes how much a random variable differs from its expected value(mean) in other words variance is defined as the average of the squares of the differences between the individual (observed) and the expected value.

**Bias**: The amount by which the expected model prediction differs from the true value of the target OR how far off our predictions are from real values.

**Variance**: The amount by which the model prediction would change if we estimate it using a different dataset. It is basically the difference in fits between training and testing dataset