From 2ecbe02d5b28ee562d10c1735244b90a08532c9e Mon Sep 17 00:00:00 2001 From: Timothy Hunter Date: Thu, 10 Dec 2015 12:50:46 -0800 Subject: [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 Closes #10234 from thunterdb/12212. --- docs/mllib-ensembles.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) (limited to 'docs/mllib-ensembles.md') diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md index 50450e05d2..2416b6fa0a 100644 --- a/docs/mllib-ensembles.md +++ b/docs/mllib-ensembles.md @@ -1,7 +1,7 @@ --- layout: global -title: Ensembles - MLlib -displayTitle: MLlib - Ensembles +title: Ensembles - spark.mllib +displayTitle: Ensembles - spark.mllib --- * Table of contents @@ -9,7 +9,7 @@ displayTitle: MLlib - Ensembles An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning) is a learning algorithm which creates a model composed of a set of other base models. -MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest). +`spark.mllib` supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest). Both use [decision trees](mllib-decision-tree.html) as their base models. ## Gradient-Boosted Trees vs. Random Forests @@ -33,9 +33,9 @@ Like decision trees, random forests handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. -MLlib supports random forests for binary and multiclass classification and for regression, +`spark.mllib` supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features. -MLlib implements random forests using the existing [decision tree](mllib-decision-tree.html) +`spark.mllib` implements random forests using the existing [decision tree](mllib-decision-tree.html) implementation. Please see the decision tree guide for more information on trees. ### Basic algorithm @@ -155,9 +155,9 @@ Like decision trees, GBTs handle categorical features, extend to the multiclass classification setting, do not require feature scaling, and are able to capture non-linearities and feature interactions. -MLlib supports GBTs for binary classification and for regression, +`spark.mllib` supports GBTs for binary classification and for regression, using both continuous and categorical features. -MLlib implements GBTs using the existing [decision tree](mllib-decision-tree.html) implementation. Please see the decision tree guide for more information on trees. +`spark.mllib` implements GBTs using the existing [decision tree](mllib-decision-tree.html) implementation. Please see the decision tree guide for more information on trees. *Note*: GBTs do not yet support multiclass classification. For multiclass problems, please use [decision trees](mllib-decision-tree.html) or [Random Forests](mllib-ensembles.html#Random-Forest). @@ -171,7 +171,7 @@ The specific mechanism for re-labeling instances is defined by a loss function ( #### Losses -The table below lists the losses currently supported by GBTs in MLlib. +The table below lists the losses currently supported by GBTs in `spark.mllib`. Note that each loss is applicable to one of classification or regression, not both. Notation: $N$ = number of instances. $y_i$ = label of instance $i$. $x_i$ = features of instance $i$. $F(x_i)$ = model's predicted label for instance $i$. -- cgit v1.2.3