Bias vs Variance
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
Bias relates to a model's difference between the true relationship between the data and the predicted value in the training set.
Variance
Variance relates to the difference between a model's prediction to the actual value.
Overfitting
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.