![]() The list is not exhaustive in either the groups or the algorithms, but I think it is representative and will be useful to you to get an idea of the lay of the land. In this section, we list many of the popular machine learning algorithms grouped the way we think is the most intuitive. ![]() I like this latter approach of not duplicating algorithms to keep things simple. We could handle these cases by listing algorithms twice or by selecting the group that subjectively is the “ best” fit. ![]() There are also categories that have the same name that describe the problem and the class of algorithm such as Regression and Clustering. There are still algorithms that could just as easily fit into multiple categories like Learning Vector Quantization that is both a neural network inspired method and an instance-based method. This is a useful grouping method, but it is not perfect. I think this is the most useful way to group algorithms and it is the approach we will use here. Algorithms Grouped By SimilarityĪlgorithms are often grouped by similarity in terms of their function (how they work). For example, tree-based methods, and neural network inspired methods. When crunching data to model business decisions, you are most typically using supervised and unsupervised learning methods.Ī hot topic at the moment is semi-supervised learning methods in areas such as image classification where there are large datasets with very few labeled examples. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.Įxample algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data. Input data is a mixture of labeled and unlabelled examples. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.Įxample problems are clustering, dimensionality reduction and association rule learning.Įxample algorithms include: the Apriori algorithm and K-Means. Input data is not labeled and does not have a known result.Ī model is prepared by deducing structures present in the input data. The training process continues until the model achieves a desired level of accuracy on the training data.Įxample problems are classification and regression.Įxample algorithms include: Logistic Regression and the Back Propagation Neural Network. Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.Ī model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. Let’s take a look at three different learning styles in machine learning algorithms: 1. This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result. ![]() There are only a few main learning styles or learning models that an algorithm can have and we’ll go through them here with a few examples of algorithms and problem types that they suit. It is popular in machine learning and artificial intelligence textbooks to first consider the learning styles that an algorithm can adopt. There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data. Plot from Wikipedia, licensed under public domain. Weak members are grey, the combined prediction is red. Kick-start your project with my new book Master Machine Learning Algorithms, including step-by-step tutorials and the Excel Spreadsheet files for all examples.Ī cool example of an ensemble of lines of best fit. The second is a grouping of algorithms by their similarity in form or function (like grouping similar animals together).īoth approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.Īfter reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related.The first is a grouping of algorithms by their learning style.I want to give you two ways to think about and categorize the algorithms you may come across in the field. There are so many algorithms that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. In this post, we will take a tour of the most popular machine learning algorithms.
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