Assume you have a classification model, training data and testing data

x_train , y_train // This is the training data
x_test , y_test // This is the testing data
y_predicted // the values predicted by the model given an input

The error rate is the average error of value predicted by the model and the correct value.

Bias

Let’s assume we have trained the model and are trying to predict values with input ‘x_train’. The predicted values are y_predicted. Bias is the error rate of y_predicted and y_train.

In simple terms,think of bias as the error rate of the training data.

When the error rate is high, we call it High Bias and when the error rate is low, we call it Low Bias

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