Classification and Regression - RDD-based API

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

<table> <thead> <tr><th>Problem Type</th><th>Supported Methods</th></tr> </thead> <tbody> <tr> <td>Binary Classification</td><td>linear SVMs, logistic regression, decision trees, random forests, gradient-boosted trees, naive Bayes</td> </tr> <tr> <td>Multiclass Classification</td><td>logistic regression, decision trees, random forests, naive Bayes</td> </tr> <tr> <td>Regression</td><td>linear least squares, Lasso, ridge regression, decision trees, random forests, gradient-boosted trees, isotonic regression</td> </tr> </tbody> </table>

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