diff options
Diffstat (limited to 'mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala')
| -rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala | 12 |
1 files changed, 6 insertions, 6 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala index 8fd6b70d4d..d8608d885d 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala @@ -71,7 +71,7 @@ abstract class Classifier[ * and put it in an RDD with strong types. * * @param dataset DataFrame with columns for labels ([[org.apache.spark.sql.types.NumericType]]) - * and features ([[Vector]]). + * and features (`Vector`). * @param numClasses Number of classes label can take. Labels must be integers in the range * [0, numClasses). * @throws SparkException if any label is not an integer >= 0 @@ -94,7 +94,7 @@ abstract class Classifier[ * by finding the maximum label value. * * Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, - * such as in [[extractLabeledPoints()]]. + * such as in `extractLabeledPoints()`. * * @param dataset Dataset which contains a column [[labelCol]] * @param maxNumClasses Maximum number of classes allowed when inferred from data. If numClasses @@ -150,7 +150,7 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur /** * Transforms dataset by reading from [[featuresCol]], and appending new columns as specified by * parameters: - * - predicted labels as [[predictionCol]] of type [[Double]] + * - predicted labels as [[predictionCol]] of type `Double` * - raw predictions (confidences) as [[rawPredictionCol]] of type `Vector`. * * @param dataset input dataset @@ -192,10 +192,10 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur /** * Predict label for the given features. - * This internal method is used to implement [[transform()]] and output [[predictionCol]]. + * This internal method is used to implement `transform()` and output [[predictionCol]]. * * This default implementation for classification predicts the index of the maximum value - * from [[predictRaw()]]. + * from `predictRaw()`. */ override protected def predict(features: FeaturesType): Double = { raw2prediction(predictRaw(features)) @@ -205,7 +205,7 @@ abstract class ClassificationModel[FeaturesType, M <: ClassificationModel[Featur * Raw prediction for each possible label. * The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives * a measure of confidence in each possible label (where larger = more confident). - * This internal method is used to implement [[transform()]] and output [[rawPredictionCol]]. + * This internal method is used to implement `transform()` and output [[rawPredictionCol]]. * * @return vector where element i is the raw prediction for label i. * This raw prediction may be any real number, where a larger value indicates greater |