A set of preference information is used as training data in the model.

This is because that decision tree model depends on the training data, which could not cover all possible corners.

The training data are used to estimate the model parameters.

This causes the network to almost always learn to reconstruct the average of all the training data.

Note that two modules, stripes 1 and 7, were not used as training data.

Prior knowledge refers to all information about the problem available in addition to the training data.

Eager learning systems also deal much better with noise in the training data.

The training data is a set of already classified samples.

In particular, a model is typically trained by maximizing its performance on some set of training data.

Also, items not seen in the training data will be given a probability of 0.0 without smoothing.