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I am new at the domain of machine learning and i have noticed that there are a lot of algorithms/ set of algorithms that can be used: SVM, decision trees, naive bayes, perceptron etc... That is why I wonder which algorithm should one use for solving which issue? In other words which algorithm solves which problem class?
So my question is if you know a good web site or book that focuses on this algorithm selection problematic?
Any help would be appreciated. Thx in advance.
Horace
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Now, to use which algorithm depends on the objective of the business problem. If inference is the goal, then restrictive models are better as they are much more interpretable. Flexible models are better if higher accuracy is the goal. In general, as the flexibility of a method increases, its interpretability decreases.
Unsupervised learning can enable an item-based recommendation system, where the learning algorithm discovers similar items bought together, for example like how Amazon looks at the people who bought book A also bought book B.
scikit-learn.org published this infographic, that can be helpful, even when you're not using sklearn library.
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@TooTone: In my opinion Machine Learning in Action could help the OP with deciding on which technique to use for a particular problem, as the book gives a clear classification of the different ML algorithms and pros, cons, and "works with" for each of them. I do agree the code is somewhat hard to read, especially for people not used to matrix operations. There is years of research condensed into a 10 line Python program, so be prepared that understanding it will take a day (for me at least).
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