-
Timothy Hunter authored
[SPARK-12212][ML][DOC] Clarifies the difference between spark.ml, spark.mllib and mllib in the documentation. Replaces a number of occurences of `MLlib` in the documentation that were meant to refer to the `spark.mllib` package instead. It should clarify for new users the difference between `spark.mllib` (the package) and MLlib (the umbrella project for ML in spark). It also removes some files that I forgot to delete with #10207 Author: Timothy Hunter <timhunter@databricks.com> Closes #10234 from thunterdb/12212.
Timothy Hunter authored[SPARK-12212][ML][DOC] Clarifies the difference between spark.ml, spark.mllib and mllib in the documentation. Replaces a number of occurences of `MLlib` in the documentation that were meant to refer to the `spark.mllib` package instead. It should clarify for new users the difference between `spark.mllib` (the package) and MLlib (the umbrella project for ML in spark). It also removes some files that I forgot to delete with #10207 Author: Timothy Hunter <timhunter@databricks.com> Closes #10234 from thunterdb/12212.
mllib-classification-regression.md 1.72 KiB
layout: global
title: Classification and Regression - spark.mllib
displayTitle: Classification and Regression - spark.mllibThe 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.
| 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: