Machine Learning Summarized in One Picture
Here is a nice summary of traditional machine learning methods, from Mathworks.
I also decided to add the following picture below, as it illustrates a method that was very popular 30 years ago but that seems to have been forgotten recently: mixture of Gaussian. In the example below, it is used to separate the data set into two clusters. Note that you can use a mixture of any distributions, not just Gaussian, for instance, (data-driven) estimated distributions such as those based on kernel density estimation.
Also, I would put neural networks in the supervised learning category.
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Comment
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Comment by Ilan Chamovitz on April 17, 2018 at 2:29pm
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Hi Vincent, good article. Please consider including a reference/credits at the bottom, in figures ;-).
If they are shared there will be a reference to the website.
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Comment by TRR on August 22, 2017 at 1:02am
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Is Random forest part of Decision Trees in the above diagram?
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Hi!
I'm agree with Dragos, the Neural networks is also used for Classification task !
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Comment by Tony Pellerin on May 28, 2017 at 3:37am
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Hello Vincent
Thank you for this article (and all the others!)
Shall we include reinforcement learning as another ML category?
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Comment by Mbaye Babacar GUEYE on April 11, 2017 at 10:46pm
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Hi!
I'm agree with Dragos.
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Comment by Lance Norskog on February 28, 2017 at 4:59pm
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The "Three Cs": Classification, Cooccurrence, Clustering. Isn't regression an implementation of classification?
Where are recommenders?
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Comment by Dragos Bandur on February 16, 2017 at 8:02am
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Hello Vincent,
I follow with interest your posts for their diversity. This diagram is very interesting and, if I may suggest, should include deep learning as a link between unsupervised and supervised learning.
Also, not all neural networks should go under supervised learning: I would add self-organizing (e.g. Kohonen) networks under unsupervised learning.
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Hello Alind,
Logistic Regression is within the GLM under Regression
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Comment by Oscar Wijsman on February 11, 2017 at 5:09am
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A decision tree gives a description of the data. The resulting classification tree can be an input for making a decision. A nice article on decision tree classifiers is this one http://mines.humanoriented.com/classes/2010/fall/csci568/portfolio_...
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Comment by Alind Agrawal on February 8, 2017 at 9:00am
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Where Logistic Regression would fall in this diagram?
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