Bias is the difference between the average prediction of our model and the correct value. If the bias value is high, then the prediction of the model is not accurate. Hence, the bias value should be as low as possible to make the desired predictions.
Variance is the number that gives the difference of prediction over a training set and the anticipated value of other training sets. High variance may lead to large fluctuation in the output. Therefore, the model’s output should have low variance.
The below diagram shows the bias–variance trade off:
What are Bias and Variance
Here, the desired result is the blue circle at the center. If we get off from the blue section, then the prediction goes wrong.