5 types of machine learning algorithms you should know
Model development is not one-size-fits-all — there are different types of machine learning algorithms for different goals and data sets. To navigate and rank specific algorithms is heavily dependent on which user role is using the algorithm and for what purpose. For example, the relatively straightforward linear regression algorithm is easier to train and implement than other machine learning algorithms, but may fail to add value in an enterprise that’s seeking to do more complex predictions.
Before diving into the complicated programming and vendor search, enterprises need to have a general understanding of model types and what they do best. The five following models range in user-friendliness and support different goals, but all are among the most popular and commonly used across enterprises.
First, and arguably the most popular type of machine learning algorithm, is linear regression. Linear regression algorithms map simple correlations between two variables in a set of data. A set of inputs and their corresponding outputs are examined and quantified to show a relationship, including how a change in one variable affects the other. Linear regressions are plotted via a line on a graph. Linear regression’s popularity is due to its simplicity: The algorithm is easily explainable, relatively transparent and requires little to no parameter tuning. Linear regression is frequently used in sales forecasting and risk assessment for enterprises that seek to make long-term business decisions. Linear regression is best for when “you are looking at predicting your value or predicting a class,” said Shekhar Vemuri, CTO of technology service company Clairvoyant, based in Chandler, Ariz. “The quintessential example is credit scoring, or will a student pass or fail a class — mostly answers that lots of enterprises need projected that come down to, ‘Is this going to happen or not?'”
A decision tree algorithm takes data and graphs it out in branches to show the possible outcomes of a variety of decisions. Decision trees classify response variables and predict response variables based on past decisions. Decision trees are a visual, easily communicated method of mapping out decisions and results and are relatively accessible to citizen data scientists. Do you need to be able to traverse decisions and see how they would affect the end result? A decision tree algorithm maps that out and can even be used with incomplete data sets through their own predictive analytics. Decision trees, due to their long-tail visuals, work best for small data sets, low-stakes decisions and concrete variables. Because of this, common decision tree use cases involve augmenting option pricing — from mortgage lenders classifying borrowers to product management teams quantifying the shift in market that would occur if they changed a major ingredient. Decision trees remain popular because they are able to outline multiple outcomes and tests without requiring data scientists to deploy multiple algorithms, said Jeff Fried, director of product management for InterSystems, a software company based in Cambridge, Mass. “Model development is not like software development.
