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Diffstat (limited to 'docs/mllib-naive-bayes.md')
-rw-r--r-- | docs/mllib-naive-bayes.md | 21 |
1 files changed, 11 insertions, 10 deletions
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index c47508b7da..4b3a7cab32 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -1,6 +1,7 @@ --- layout: global -title: <a href="mllib-guide.html">MLlib</a> - Naive Bayes +title: Naive Bayes - MLlib +displayTitle: <a href="mllib-guide.html">MLlib</a> - Naive Bayes --- Naive Bayes is a simple multiclass classification algorithm with the assumption of independence @@ -27,11 +28,11 @@ sparsity. Since the training data is only used once, it is not necessary to cach <div class="codetabs"> <div data-lang="scala" markdown="1"> -[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements +[NaiveBayes](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional +[LabeledPoint](api/scala/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an optional smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which +[NaiveBayesModel](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which can be used for evaluation and prediction. {% highlight scala %} @@ -59,11 +60,11 @@ val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test <div data-lang="java" markdown="1"> -[NaiveBayes](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayes$) implements +[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements multinomial naive Bayes. It takes a Scala RDD of -[LabeledPoint](api/mllib/index.html#org.apache.spark.mllib.regression.LabeledPoint) and an +[LabeledPoint](api/java/org/apache/spark/mllib/regression/LabeledPoint.html) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/mllib/index.html#org.apache.spark.mllib.classification.NaiveBayesModel), which +[NaiveBayesModel](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html), which can be used for evaluation and prediction. {% highlight java %} @@ -102,11 +103,11 @@ double accuracy = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Do <div data-lang="python" markdown="1"> -[NaiveBayes](api/pyspark/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial +[NaiveBayes](api/python/pyspark.mllib.classification.NaiveBayes-class.html) implements multinomial naive Bayes. It takes an RDD of -[LabeledPoint](api/pyspark/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally +[LabeledPoint](api/python/pyspark.mllib.regression.LabeledPoint-class.html) and an optionally smoothing parameter `lambda` as input, and output a -[NaiveBayesModel](api/pyspark/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be +[NaiveBayesModel](api/python/pyspark.mllib.classification.NaiveBayesModel-class.html), which can be used for evaluation and prediction. <!-- TODO: Make Python's example consistent with Scala's and Java's. --> |