Train a classifier on textual data:

Classify new examples:

Train a classifier on image examples, gathered by their class:

Classify new examples:

Obtain probabilities of a given class for new examples:

Train a classifier on data where the feature is a sequence of tokens:

Classify a new example:

Train a classifier on a dataset with features and classes in separate lists:

Obtain information about the classifier with Information:

Generate a ClassifierMeasurements[…] object of the classifier applied to a test set:

Get the accuracy from the classifier measurements object:

Visualize the confusion matrix:

Train a classifier on a dataset with missing features:

Classify a new example:

Classify examples containing missing features:

Train a classifier on a dataset with named features. The order of the keys does not matter. Keys can be missing:

Classify a new example:

Classify examples containing missing features:

Construct a Dataset with a list of associations:

Train a classifier to predict the feature "gender" as function of the other features:

Once the classifier is trained, any input format can be used. Classify an example formatted as an association:

Find out the order of the features, and classify an example formatted as a list:

Classify examples in a Dataset:

Create an artificial dataset from three normally distributed clusters:

Train a classifier on this dataset:

Plot the training set and the probability distribution of each class as a function of the features: