Mathematically the error emerging from any model can be broken down into 3 major components. Error(X) = Square(Bias) + Variance + Irreducible Error It is important to handle or address the bias error and variance error which is in control. We can’t do much for irreducible error. Low Bias - indicates fewer assumptions about the form of the target variable or function. In this case, when we test on new data, it does not give expected results and accuracy can be compromised. High Bias indicates high assumptions in a similar context. High variance - indicates large changes to the estimate of target variable or target function with changes to the training data. Low variance indicates smaller changes to the estimate of the target variable or target function in a similar context. When we are trying to build a model with greater accuracy, for better performance of the model, it is critical to strike a balance between bias and variance so that errors can be minimized and the gap between actual and predicted outcomes can be reduced. Hence balance between Bias and Variance needs to be maintained.