Overfitting is a problem that occurs with machine learning algorithms that are able to generate very accurate results on a training dataset but fail to generalize very well from what they've learned. We say that models which have overfit the data have very high variance. When we trained our decision tree on data that included the numeric age of passengers, we were overfitting the data.
Conversely, certain models may have very high bias. This is a situation where the model has a strong tendency towards a certain outcome irrespective of the training examples to the contrary. Recall our example of a classifier that always predicts that a survivor will perish. This classifier would perform well on dataset with low survivor rates, but very poorly otherwise.
In the case of high bias, the model is unlikely to perform well on diverse inputs at the training stage. In the case of high variance, the model is unlikely to perform well on data that differs from that which it was trained...
