name: MulticlassClassification kind: supervised-learning taskDescription: |- The goal of this task is to learn how to classify data points represented as real vectors into one of K classes. datasetDescription: |- One file where each line corresponds to an example:
  output featureIndex:featureValue ... featureIndex:featureValue 
  
where featureIndex is a positive integer, featureValue is a real number, and output ∈ {1, 2, ..., K}. The feature indices must be sorted in increasing order. For the test file, output is 0. The predictions file contains a line for each test example:
  predicted-output
  
sampleDataset: multiclass-sample utilsProgram: multiclass-utils evaluatorProgram: classification-evaluator datasetFields: - name: "#train" type: integer value: train/numExamples description: Number of training examples. - name: "#test" type: integer value: test/numExamples description: Number of test examples. - name: dim type: integer value: train/numDim description: Number of input dimensions (features). - name: K type: integer value: train/numLabels description: Number of output classes (labels). runFields: - name: Learn time type: time value: learn/time description: Time to learn the model on training data. - name: Train error type: double value: doTrain/evaluate/errorRate description: Fraction of misclassified training examples. - name: Predict train time type: time value: doTrain/predict/time description: Time took to predict on the training set. - name: Test error type: double value: doTest/evaluate/errorRate description: Fraction of misclassified test examples. - name: Predict test time type: time value: doTest/predict/time description: Time to predict on the test set. errorFieldValue: doTest/evaluate/errorRate reductions: - program: one-vs-all args: BinaryClassification