Categorisation of Machine Learning algorithms for business applications

Practicing the scientific approach to the data exploration one should know at what extent certain method can be applied. Neural Nets are futile for the stock market’s predictions. Monte-Carlo algorithms couldn’t offer much help either, and poorly implemented Random Forest algorithm can literally ruin your vacation in South-East Asia, especially if it was implemented by NSA. In this article we will briefly introduce machine learning methods classification and see how they are relevant to the different lines of business.

From the cradle to the grave, we are making decisions – from our first decision to attract mother’s attention to one of our last decisions when asking the doctor for pain treatment. Decision making is an essential part of our life, with motivation (conscious or somewhat fuzzy) as the background of this process. Business is no different in this sense – as our computerized “decision maker” we can use a Rule Engine (BRE) and some preceding logic. Not long ago BRE were perceived as a paramount part of business intelligence (BI). The truth is, the implementation of the effective BRE can be exceedingly simple, as it was demonstrated in [2]. Using IoC (Java) or NDS (OraDB) lightweight BRE can be implemented right where it is needed (close to the objects change location, i.e. event source), code samples and supporting DB structures are available for download. Quite often, however, external BRE will just complicate the situation, adding complexity to the technical infrastructure, require object serialisation/deserialization, imposing addition costs (sometimes quite high). Most importantly, classic BRE just answers to the clearly formulated questions in “Yes/No” fashion, contributing only a little to the question formulation. Even worse if the logic is fuzzy and/or based on random data. Thus, building the motivation for decision making is not only the most important part of BI, but also the hardest one, as it will require statistical data analysis with elements of prediction and simulation using adaptive algorithms.

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“There are no right answers to wrong questions.” – Ursula K. Le Guin

Prediction, simulation and adaptation denote the presence the learning capabilities, see Arthur Samuel’s definition in the following table. Gnosiology (philosophical concept dedicated to theory of knowledge) identifies three distinctive knowledge gaining approaches: supervised, unsupervised and reinforced. Each approaches the problem with its own set of methods and algorithms, with different levels of applicability, depending on problem at hand. There are no strict borders between sets of methods and since the total number of statistical and learning algorithms is more than 700, it is simply not possible even to mention a half of them in a short blogpost. Here I will just try to associate and group the learning methods and most common algorithms with business areas of applicability, starting from ML approaches of gaining knowledge.

For simplicity I will use one example from classic AI books [3], the fairy tale, where Prince Charming is searching Sleeping Beauty in a Kingdom Far-Far-Away with some help of know-it-all Owl, capable to say only “Yes” and “No” (see BRE above).

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Using the kingdom’s map and wise Owl with spoken language disorder, Prince Charming could use bisection method, dividing the map in half and asking Owl “where is the princess” repeatedly, until the last half will be of the size of an average cave. So here, Prince Charming gets help from the supervisor, Bisection Regression Isolation algorithm from the Regression group of algorithms. This is a quite broad group of algorithms, including Linear Regression with Single or Multiple Variables.

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