What are variance and bias

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