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Supervised machine learning models can be an efficient and cost-effective way of solving data-driven problems, because the human expert needs to provide only a few labeled data points, and the labeled data is sufficient to train a model. The human expert, or author, labels training data when looking at a picture, document, etc that is about a specific problem. The data is called labeled, accurate, and relevant. Other types of data, like images, are called unstructured and unlabeled.

This section discusses the importance of evaluating unseen data or unknown data, where few or no labeled data points are available. Some of the things to worry about in this scenario are overfitting or forgetting. Overfitting refers to a machine learning model learning too much from the training data that it reflects the training data poorly when it is applied to more general data or new data.

For example, a machine learning model that has learned from a dataset where the step is a few hundred miles might be expected to think that the step is still a few hundred miles distance. But when you try a step of a mile, the model does not know that the data it has is about a mile long and therefore it may promise you a distance that is longer than what the step really is, and the machine stops outside of the step.

Overfitting is obvious where a machine learning model learns from training data, but uses that bad information for real-world data. If a machine learning model has overfit data, it may give predictions that are faulty.

When we say “overfit”, it could be a situation where the model learns too much from the training data and does not respond well to other data, or it learns too much from the training data and learns something that is totally irrelevant to the real-world problem. This lack of transferability can occur when the model is overfit. d2c66b5586