Create a ClassifierFunction with Classify and a list of labeled examples:

Classify an unlabeled example with the ClassifierFunction:

Classify multiple examples:

Return the probabilities of the classes given the feature of an example:

Return the sorted probabilities of the most likely classes:

Return the probability of the most probable class:

Return the probability of a given class:

Plot the probability of class "B" as a function of the feature:

Generate a ClassifierFunction using multiple features:

Use the function on a new example:

Classify an example that has missing features:

Get the probabilities for the most probable classes:

Create a function classifying textual data:

Classify new examples:

Obtain information on the function:

Obtain the properties that can be used by this function:

Train a classifier function:

Generate a classifier measurements object of the function applied to a test set:

Get the accuracy from the function on the test set:

Visualize the confusion matrix:

Generate a classifier function whose input is an association:

Use the function on an example:

Classify examples containing missing features:

Train a classifier:

Store the ClassifierFunction[…] into a file using the "WMLF" format:

Load the classifier from the file using Import:

Use the loaded classifier on new data:

Train a classifier to predict a person's odds of surviving or dying in the Titanic crash:

Calculate the prior odds of a passenger dying:

Use the classifier to predict the odds of a person dying:

Get an explanation of how each feature multiplied the model's predicted odds of a class:

Compare the model's explanation of feature impact to the base rate odds: