Are you a machine learning enthusiast and also want to play darts? Well, the right time on the right page.

Let us keep this article clear and simple.

Bias:

It is the simplistic assumption made by the model. It means the model is learning the wrong way to predict without learning all of the information from the data. This can possibly lead the model to the condition of underfitting for unseen data.

Variance:

It is the degree of complexity undergone by the model. It means the model has a tendency of sensitivity towards the data. This also tells how scattered is the predicted value from the actual value. More sparse the data, the more the variance. The more the variance, the more is the complexity undergone by the model.

A game of darts in Bias and Variance?

Let us assume there are four players in darts who belong to different levels as said below: