AntonAntonov/ MonadicContextualClassification

A monad for machine learning classification workflows

Contributed by: Anton Antonov

This software monad helps with Machine Learning (ML) classification workflows. It makes it easy to bring in data, supports different types of classifiers, and allows for the hands-on use of Receiver Operating Characteristic (ROC) functions. It also includes other useful features for measuring and performing classification operations in ML.

Installation Instructions

To install this paclet in your Wolfram Language environment, evaluate this code:
PacletInstall["AntonAntonov/MonadicContextualClassification"]

To load the code after installation, evaluate this code:
Needs["AntonAntonov`MonadicContextualClassification`"]

Details

The monad facilitates the rapid specification of classification workflows.

A typical (short) workflow is: split the data, make a classifier, compute and show classifier measurements, show ROC plots.

If the given data is a dataset, then by default the last column is considered to have the class labels.

The data splitting into training and testing subsets utilizes random sampling.

By default class-label stratification is used for the data splitting.

Both built-in classifiers and ensemble classifiers can be used in monads pipelines.

The computation of the (standard) built-in classifier measurements is supported.

Special monad functions are used for the ROC plots.

Paclet Guide

Examples

Basic Examples (3)

Here is a dataset:

In[1]:=

dsTitanic = ResourceFunction["ExampleDataset"][{"MachineLearning", "Titanic"}];
dsTitanic[[100 ;; 108 ;; 2]]

Out[2]=

Here is a classification pipeline:

In[3]:=

(* Evaluate this cell to get the example input *) CloudGet["https://www.wolframcloud.com/obj/28176d7f-9a21-4d94-9275-a6b6e85141a3"]

The ClCon object is summarized with MakeBoxes:

In[4]:=

Out[4]=

Scope (3)

Context (1)

The ClCon object is an association with the keys:

In[5]:=

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