org.apache.spark.SparkException: Currently, LogisticRegression with ElasticNet in ML package only supports binary classification. Found 5 in the input dataset.

  1. 0

    Logistic Regression as multiclass classification using PySpark and issues

    Data Science | 3 months ago | krishna Prasad
    org.apache.spark.SparkException: Currently, LogisticRegression with ElasticNet in ML package only supports binary classification. Found 5 in the input dataset.
  2. 0

    Logistic Regression as multiclass classification using PySpark and issues

    Stack Overflow | 3 months ago | krishna Prasad
    org.apache.spark.SparkException: Currently, LogisticRegression with ElasticNet in ML package only supports binary classification. Found 5 in the input dataset.
  3. 0

    Issues with Logistic Regression for multiclass classification using PySpark

    Stack Overflow | 3 months ago | krishna Prasad
    org.apache.spark.SparkException: Currently, LogisticRegression with ElasticNet in ML package only supports binary classification. Found 5 in the input dataset.
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  5. 0

    Error on using glm with SparkR

    Stack Overflow | 9 months ago | hbabbar
    org.apache.spark.SparkException: Currently, LogisticRegression with ElasticNet in ML package only supports binary classification. Found 5 in the input dataset.
  6. 0

    Multiple SparkContext detected in the same JVM

    Stack Overflow | 10 months ago | Guforu
    org.apache.spark.SparkException: Only one SparkContext may be running in this JVM (see SPARK-2243). To ignore this error, set spark.driver.allowMultipleContexts = true. The currently running SparkContext was created at: org.apache.spark.SparkContext.<init>(SparkContext.scala:81) test.MLlib.BinarryClassification.main(BinaryClassification.java:41)

  1. tyson925 1 times, last 1 month ago
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Root Cause Analysis

  1. org.apache.spark.SparkException

    Currently, LogisticRegression with ElasticNet in ML package only supports binary classification. Found 5 in the input dataset.

    at org.apache.spark.ml.classification.LogisticRegression.train()
  2. Spark Project ML Library
    Predictor.fit
    1. org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:290)
    2. org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:159)
    3. org.apache.spark.ml.Predictor.fit(Predictor.scala:90)
    4. org.apache.spark.ml.Predictor.fit(Predictor.scala:71)
    4 frames
  3. Java RT
    Method.invoke
    1. sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    2. sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    3. sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    4. java.lang.reflect.Method.invoke(Method.java:498)
    4 frames
  4. Py4J
    GatewayConnection.run
    1. py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
    2. py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
    3. py4j.Gateway.invoke(Gateway.java:259)
    4. py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
    5. py4j.commands.CallCommand.execute(CallCommand.java:79)
    6. py4j.GatewayConnection.run(GatewayConnection.java:209)
    6 frames
  5. Java RT
    Thread.run
    1. java.lang.Thread.run(Thread.java:745)
    1 frame