Description

Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

Parameters

Name Description Type Required？ Default Value
numClass num class of multi class train. Integer ✓
predictionCol Column name of prediction. String ✓
predictionDetailCol Column name of prediction result, it will include detailed info. String
reservedCols Names of the columns to be retained in the output table String[] null

Script Example

Code

  1. URL = "http://alink-dataset.cn-hangzhou.oss.aliyun-inc.com/csv/iris.csv";
  2. SCHEMA_STR = "sepal_length double, sepal_width double, petal_length double, petal_width double, category string";
  3. data = CsvSourceBatchOp().setFilePath(URL).setSchemaStr(SCHEMA_STR)
  5. lr = LogisticRegression() \
  6.     .setFeatureCols(["sepal_length", "sepal_width", "petal_length", "petal_width"]) \
  7.     .setLabelCol("category") \
  8.     .setMaxIter(100)
  10. oneVsRest = OneVsRest().setClassifier(lr).setNumClass(3)
  11. model = oneVsRest.fit(data)
  12. model.setPredictionCol("pred_result").setPredictionDetailCol("pred_detail")
  13. model.transform(data).print()