How to choose classifier on specific dataset

Question

When given the dataset, normally m instances by n features matrix, how to choose the classifier that is most appropriate for the dataset.

Answer 1

This is just like what algorithm to solve a prime Number. Not every algorithm solve any problem means each problem assigned which finite no. of algorithm. In machine learning you can apply different algorithm on a type of problem.

If matrix contain real numbered features then you can use KNN algorithm can be used. Or if matrix have words as feature then you can use naive bayes classifier which is one of best for text classification. And Machine learning have tons of algorithm you can read them apply to your problem which fits best. Hope you understand what I said.

Answer 2

An interesting but much more general map I found:

http://scikit-learn.org/stable/tutorial/machine_learning_map/

Answer 3

If you have weka, you can use experimenter and choose different algorithms on same data set to evaluate different models.

Answer 4

This project compares many different classifiers on different typical datasets.

If you have no idea, you could use this simple tool auto-weka which will test all the different classifiers you selected within different constraints. Before using auto-weka, you may need to convert your data to ARFF using Weka or just manually (many tutorial on youtube).

The best classifier depends on your data (binary/string/real/tags, patterns, distribution...), what kind of output to predict (binary class / multi-class / evolving classes / a value from regression ?) and the expected performance (time, memory, accuracy). It would also depend on whether you want to update your model frequently or not (ie. if it is a stream, better use an online classifier).

Please note that the best classifier may not be one but an ensemble of different classifiers.