When building a learning algorithm, we want it to work well on the future data, not on the training data. Many algorithms will make perfect predictions on the training data, but perform poorly on the future data. This is known as overfitting. In this video we provide formal definitions of over-fitting and under-fitting and give examples for classification and regression tasks.