The error introduced in your model because of over-simplification of the algorithm is known as Bias. On the other hand, Variance is the error introduced to your model because of the complex nature of machine learning algorithm. In this case, the model also learns noise and perform poorly on the test dataset.
The bias-variance tradeoff is the optimum balance between bias and variance in a machine learning model. If you try to decrease bias, the variance will increase and vice-versa.
Total Error= Square of bias+variance+irreducible error. Bias variance tradeoff is the process of finding the exact number of features while model creation such that the error is kept minimum, but also taking effective care such that the model does not overfit or underfit.
