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Diffstat (limited to 'docs/mllib-naive-bayes.md')
| -rw-r--r-- | docs/mllib-naive-bayes.md | 9 |
1 files changed, 5 insertions, 4 deletions
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index 9780ea52c4..56a2e9ca86 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -21,7 +21,7 @@ Within that context, each observation is a document and each feature represents a term whose 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 nonnegative. The model type is selected with an optional parameter -"Multinomial" or "Bernoulli" with "Multinomial" as the default. +"multinomial" or "bernoulli" with "multinomial" as the default. [Additive smoothing](http://en.wikipedia.org/wiki/Lidstone_smoothing) can be used by setting the parameter $\lambda$ (default to $1.0$). For document classification, the input feature vectors are usually sparse, and sparse vectors should be supplied as input to take advantage of @@ -35,7 +35,7 @@ sparsity. Since the training data is only used once, it is not necessary to cach [NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of [LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional -smoothing parameter `lambda` as input, an optional model type parameter (default is Multinomial), and outputs a +smoothing parameter `lambda` as input, an optional model type parameter (default is "multinomial"), and outputs a [NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. @@ -54,7 +54,7 @@ val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) val training = splits(0) val test = splits(1) -val model = NaiveBayes.train(training, lambda = 1.0, model = "Multinomial") +val model = NaiveBayes.train(training, lambda = 1.0, model = "multinomial") val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() @@ -75,6 +75,8 @@ optionally smoothing parameter `lambda` as input, and output a can be used for evaluation and prediction. {% highlight java %} +import scala.Tuple2; + import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.function.Function; @@ -82,7 +84,6 @@ import org.apache.spark.api.java.function.PairFunction; import org.apache.spark.mllib.classification.NaiveBayes; import org.apache.spark.mllib.classification.NaiveBayesModel; import org.apache.spark.mllib.regression.LabeledPoint; -import scala.Tuple2; JavaRDD<LabeledPoint> training = ... // training set JavaRDD<LabeledPoint> test = ... // test set |