Summary:
When we train a model, we split data into a testing set and a training set. We train our model against the training set and then evaluate the predictions of the model against the actual values in the testing set.
Bias relates to a model’s difference between the true relationship between the data and the predicted value in the training set.
Variance relates to the difference between a models prediction to the actual value
Overfitting is a problem where a model adapts too much to the training set of the data and lacks the ability to predict the testing data