Define a function that checks whether the ASCII character corresponding to an integer is a letter, then define a set of configurations:

Construct an initial training set:

Train a classification object to classify whether an integer corresponds to a letter by including the preceding information:

The examples in the first row in the training history now correspond to the initial training set:

Train a classification object to classify whether a number in a given interval is positive:

Obtain the number of function evaluations:

Specify the maximum number of iterations:

Check the number of function evaluations now:

Define a region composed of a number of subregions, and a function that tells whether the blood pressure corresponding to a point in the region is "Low", "Ideal" or "High":

Train a classification object by specifying the method:

The configurations are explored randomly, with the data generating distribution:

Specify a different method for active classification:

The algorithm now prefers to explore configurations near the boundaries where the model is more uncertain:

Specify the method as an association, choosing the evaluation strategy and the classification method:

Define a "grade function" that gives a letter grade corresponding to a real score between 0 and 10:

Train a classification object to classify letter grades, with the domain specified by a configuration generator:

Obtain the classifier:

Display the confusion matrix of the classifier on a test set:

Construct a geometric region corresponding to the map of the USA:

Define a function that tells whether a point lies inside this region:

Train a classification object for this function:

Obtain the corresponding classifier:

Plot the configurations explored during training. The density is higher close to the region boundary:

Visualize the classification region:

Define two programs to compute the ManhattanDistance between two elements:

Define a function that selects the program that more quickly computes the ManhattanDistance between two elements constructed from the three integers:

Set the domain of the function via a configuration generator:

Train a classification object to classify which program is faster:

Obtain the classifier:

Define a test set:

Display the accuracy and confusion matrix of the classifier on the test set: