mllib-classification-regression.md

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global	Classification and Regression - MLlib	<a href="mllib-guide.html">MLlib</a> - Classification and Regression

MLlib supports various methods for binary classification, multiclass classification, and regression analysis. The table below outlines the supported algorithms for each type of problem.

Problem Type	Supported Methods
Binary Classification	linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes
Multiclass Classification	logistic regression, decision trees, random forests, naive Bayes
Regression	linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression

More details for these methods can be found here:

• Linear models
  • classification (SVMs, logistic regression)
  • linear regression (least squares, Lasso, ridge)
• Decision trees
• Ensembles of decision trees
  • random forests
  • gradient-boosted trees
• Naive Bayes
• Isotonic regression