Balancing Bias and Variance to Control Errors in Machine Learning

In the world of Machine Learning, accuracy is everything. You strive to make your model more accurate by tuning and tweaking the parameters, but are never able to make it 100% accurate. That's the hard truth about your prediction/ classification models, they can never be error free. In this article I'll discuss why this happens and other forms of error that can be reduced. Suppose we are observing a response variable Y (qualitative or quantitative) and input variable X having p number of features or columns (X1, X2…..Xp) and we assume there is relation between them.