Repository: spark Updated Branches: refs/heads/master 6ea582e36 -> 94155d039
[MINOR][ML][DOC] Improved Naive Bayes user guide explanation ## What changes were proposed in this pull request? This copies the material from the spark.mllib user guide page for Naive Bayes to the spark.ml user guide page. I also improved the wording and organization slightly. ## How was this patch tested? Built docs locally. Author: Joseph K. Bradley <jos...@databricks.com> Closes #21272 from jkbradley/nb-doc-update. Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/94155d03 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/94155d03 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/94155d03 Branch: refs/heads/master Commit: 94155d0395324a012db2fc8a57edb3cd90b61e96 Parents: 6ea582e Author: Joseph K. Bradley <jos...@databricks.com> Authored: Wed May 9 10:34:57 2018 -0700 Committer: Joseph K. Bradley <jos...@databricks.com> Committed: Wed May 9 10:34:57 2018 -0700 ---------------------------------------------------------------------- docs/ml-classification-regression.md | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/94155d03/docs/ml-classification-regression.md ---------------------------------------------------------------------- diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md index d660655..b3d1090 100644 --- a/docs/ml-classification-regression.md +++ b/docs/ml-classification-regression.md @@ -455,11 +455,29 @@ Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classificat ## Naive Bayes [Naive Bayes classifiers](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) are a family of simple -probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence -assumptions between the features. The `spark.ml` implementation currently supports both [multinomial -naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html) +probabilistic, multiclass classifiers based on applying Bayes' theorem with strong (naive) independence +assumptions between every pair of features. + +Naive Bayes can be trained very efficiently. With a single pass over the training data, +it computes the conditional probability distribution of each feature given each label. +For prediction, it applies Bayes' theorem to compute the conditional probability distribution +of each label given an observation. + +MLlib supports both [multinomial naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier#Multinomial_naive_Bayes) and [Bernoulli naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html). -More information can be found in the section on [Naive Bayes in MLlib](mllib-naive-bayes.html#naive-bayes-sparkmllib). + +*Input data*: +These models are typically used for [document classification](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html). +Within that context, each observation is a document and each feature represents a term. +A feature's value is the frequency of the term (in multinomial Naive Bayes) or +a zero or one indicating whether the term was found in the document (in Bernoulli Naive Bayes). +Feature values must be *non-negative*. The model type is selected with an optional parameter +"multinomial" or "bernoulli" with "multinomial" as the default. +For document classification, the input feature vectors should usually be sparse vectors. +Since the training data is only used once, it is not necessary to cache it. + +[Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by +setting the parameter $\lambda$ (default to $1.0$). **Examples** --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org
