class NaiveBayesModel extends ClassificationModel with Serializable with Saveable
Model for Naive Bayes Classifiers.
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- @Since("0.9.0")
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- val labels: Array[Double]
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- val modelType: String
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- val pi: Array[Double]
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- def predict(testData: Vector): Double
Predict values for a single data point using the model trained.
Predict values for a single data point using the model trained.
- testData
array representing a single data point
- returns
predicted category from the trained model
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- NaiveBayesModel → ClassificationModel
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- @Since("1.0.0")
- def predict(testData: RDD[Vector]): RDD[Double]
Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
- testData
RDD representing data points to be predicted
- returns
an RDD[Double] where each entry contains the corresponding prediction
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- NaiveBayesModel → ClassificationModel
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- @Since("1.0.0")
- def predict(testData: JavaRDD[Vector]): JavaRDD[Double]
Predict values for examples stored in a JavaRDD.
Predict values for examples stored in a JavaRDD.
- testData
JavaRDD representing data points to be predicted
- returns
a JavaRDD[java.lang.Double] where each entry contains the corresponding prediction
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- ClassificationModel
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- @Since("1.0.0")
- def predictProbabilities(testData: Vector): Vector
Predict posterior class probabilities for a single data point using the model trained.
Predict posterior class probabilities for a single data point using the model trained.
- testData
array representing a single data point
- returns
predicted posterior class probabilities from the trained model, in the same order as class labels
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- @Since("1.5.0")
- def predictProbabilities(testData: RDD[Vector]): RDD[Vector]
Predict values for the given data set using the model trained.
Predict values for the given data set using the model trained.
- testData
RDD representing data points to be predicted
- returns
an RDD[Vector] where each entry contains the predicted posterior class probabilities, in the same order as class labels
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- @Since("1.5.0")
- def save(sc: SparkContext, path: String): Unit
Save this model to the given path.
Save this model to the given path.
This saves:
- human-readable (JSON) model metadata to path/metadata/
- Parquet formatted data to path/data/
The model may be loaded using
Loader.load.- sc
Spark context used to save model data.
- path
Path specifying the directory in which to save this model. If the directory already exists, this method throws an exception.
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- NaiveBayesModel → Saveable
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- Deprecated
(Since version 9)