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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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.
Is Random forest part of Decision Trees in the above diagram?
Hi!
I'm agree with Dragos, the Neural networks is also used for Classification task !
Hi!
I'm agree with Dragos.
The "Three Cs": Classification, Cooccurrence, Clustering. Isn't regression an implementation of classification?
Where are recommenders?
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.
Hello Alind,
Logistic Regression is within the GLM under Regression
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_...
Posted 12 April 2021
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