In the simplest terms, Bias is the difference between the Predicted Value and the Expected Value. To explain further, the model makes certain assumptions when it trains on the data provided. When it is introduced to the testing/validation data, these assumptions may not always be correct.

In our model, if we use a large number of nearest neighbors, the model can totally decide that some parameters are not important at all. For example, it can just consider that the Glusoce level and the Blood Pressure decide if the patient has diabetes. This model would make very strong assumptions about the other parameters not affecting the outcome. You can also think of it as a model predicting a simple relationship when the datapoints clearly indicate a more complex relationship.

Mathematically, let the input variables be X and a target variable Y. We map the relationship between the two using a function f.

Therefore,

**Y = f(X) + e**

Here ‘e’ is the error that is normally distributed. The aim of our model f’(x) is to predict values as close to f(x) as possible. Here, the Bias of the model is:

**Bias[f’(X)] = E[f’(X) – f(X)]**

As I explained above, when the model makes the generalizations i.e. when there is a high bias error, it results in a very simplistic model that does not consider the variations very well. Since it does not learn the training data very well, it is called **Underfitting**.