Waffles (machine learning)

Waffles
Developer(s)	Michael S. Gashler
Operating system	Cross-platform
Available in	C++
Type	Machine Learning
License	GNU Lesser General Public License
Website	http://csce.uark.edu/~mgashler/waffles/

Last updated September 12, 2019

Waffles is a collection of command-line tools for performing machine learning operations developed at Brigham Young University. These tools are written in C++, and are available under the GNU Lesser General Public License.

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.

Brigham Young University is a private research university located in Provo, Utah and owned by The Church of Jesus Christ of Latter-day Saints. The university is run under the auspices of its parent-organization, the Church Educational System (CES), and is classified among "Doctoral Universities: High Research Activity" with "more selective, lower transfer-in" admissions. The university's primary emphasis is on undergraduate education in 179 majors, but it also has 62 master's and 26 doctoral degree programs. The university also administers two satellite campuses, one in Jerusalem and one in Salt Lake City.

C++ is a general-purpose programming language created by Bjarne Stroustrup as an extension of the C programming language, or "C with Classes". The language has expanded significantly over time, and modern C++ has object-oriented, generic, and functional features in addition to facilities for low-level memory manipulation. It is almost always implemented as a compiled language, and many vendors provide C++ compilers, including the Free Software Foundation, LLVM, Microsoft, Intel, and IBM, so it is available on many platforms.

Description

The Waffles machine learning toolkit^[1] contains command-line tools for performing various operations related to machine learning, data mining, and predictive modeling. The primary focus of Waffles is to provide tools that are simple to use in scripted experiments or processes. For example, the supervised learning algorithms included in Waffles are all designed to support multi-dimensional labels, classification and regression, automatically impute missing values, and automatically apply necessary filters to transform the data to a type that the algorithm can support, such that arbitrary learning algorithms can be used with arbitrary data sets. Many other machine learning toolkits provide similar functionality, but require the user to explicitly configure data filters and transformations to make it compatible with a particular learning algorithm. The algorithms provided in Waffles also have the ability to automatically tune their own parameters (with the cost of additional computational overhead).

Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information from a data set and transform the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient. Classification is an example of pattern recognition.

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed.

Because Waffles is designed for script-ability, it deliberately avoids presenting its tools in a graphical environment. It does, however, include a graphical "wizard" tool that guides the user to generate a command that will perform a desired task. This wizard does not actually perform the operation, but requires the user to paste the command that it generates into a command terminal or a script. The idea motivating this design is to prevent the user from becoming "locked in" to a graphical interface.

All of the Waffles tools are implemented as thin wrappers around functionality in a C++ class library. This makes it possible to convert scripted processes into native applications with minimal effort.

Waffles was first released as an open source project in 2005. Since that time, it has been developed at Brigham Young University, with a new version having been released approximately every 6–9 months. Waffles is not an acronym—the toolkit was named after the food for historical reasons.

Advantages

Some of the advantages of Waffles in contrast with other popular open source machine learning toolkits include:

Waffles automatically takes care of many issues related to data format in order to simplify its tools.
Because it is implemented in C++, many of its algorithms are particularly fast. Also, the lack of dependency on any virtual machine makes it easier to deploy in conjunction with other applications.
The functionality included in Waffles is very broad, including algorithms for dimensionality reduction, collaborative filtering, visualization, clustering, supervised learning, optimization, linear algebra, data transformation, image and signal processing, policy learning, and sparse matrix operations.

Disadvantages

Although Waffles provides significant breadth, it lacks the depth of many toolkits that focus on a particular area of machine learning. The Weka (machine learning) toolkit, for example, provides many more classification algorithms than Waffles provides.
Waffles only has a limited graphical interface.

Waikato Environment for Knowledge Analysis (Weka) is a suite of machine learning software written in Java, developed at the University of Waikato, New Zealand. It is free software licensed under the GNU General Public License.

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References

↑ Gashler, Michael S. (2011). "Waffles: A Machine Learning Toolkit" (PDF). Journal of Machine Learning Research. JMLR.org and Microtome Publishing. 12 (1532–4435): 2383–2387.

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[1] Gashler, Michael S. (2011). "Waffles: A Machine Learning Toolkit" (PDF). Journal of Machine Learning Research. JMLR.org and Microtome Publishing. 12 (1532–4435): 2383–2387.