This documentation is for Machine Learner 1.0.0. View documentation for the latest release.
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One of the most common questions that arise in machine learning problems is that, which algorithm you should use to model a given set of data. Most of the time, there is no straight forward answer, as it depends on several factors of the dataset such as the type and properties of the features, size of the data, as well as the objective of fitting the model etc. Following diagram provides a general guideline on how to select a suitable algorithm for your dataset, in WSO2 Machine Learner.

selecting an algorithm

Use the above guidelines to find an algorithm to build a model to fit your data. However, if you need to find the best model, then try out a few algorithms in the same class and see which one performs better.

Algorithm types

The following table explains the algorithm types in the above diagram.

Algorithm TypeDescriptionSupported Measures
Numerical PredictionThis involves making a numerical prediction based on the dataset analysed.
Multi-class classificationThis involves classifying the items in a dataset into multiple categories.
Binary classificationThis involves classifying  the data items in a dataset into two categories.
ClusteringThis involves clustering the items in a dataset.No evaluation measures are available for ML 1.0.0.
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