Analytics for the Internet of Things (IoT)

Generalization

The fundamental goal of any ML project is to produce a model that operates well with datasets beyond what is available to you currently. You want it to predict, classify, or estimate with minimal errors. Not so much on the data you already have, but on data that you will have when the resulting model is implemented.

The data that you already have to develop your ML model is called the training set. The training set should be representative of what you expect to find in the datasets that you will be applying the model to in the future. Even if you have a very large training set, it is unlikely that future datasets will be precisely the same. This great big, complex world produces a lot of variation.

The ability of an ML model ...