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problemDoc.json 1.0 kB

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  1. {
  2. "about": {
  3. "problemID": "multi_input_problem",
  4. "problemName": "Problem associate with multiple dataset",
  5. "problemDescription": "Distinguish Iris flowers of three related species.",
  6. "problemSchemaVersion": "4.0.0",
  7. "problemVersion": "4.0.0",
  8. "taskKeywords": [
  9. "classification",
  10. "multiClass"
  11. ]
  12. },
  13. "inputs": {
  14. "data": [
  15. {
  16. "datasetID": "iris_dataset_1",
  17. "targets": [
  18. {
  19. "targetIndex": 0,
  20. "resID": "learningData",
  21. "colIndex": 5,
  22. "colName": "species"
  23. }
  24. ]
  25. },
  26. {
  27. "datasetID": "boston_dataset_1",
  28. "targets": [
  29. {
  30. "targetIndex": 0,
  31. "resID": "learningData",
  32. "colIndex": 14,
  33. "colName": "MEDV"
  34. }
  35. ]
  36. }
  37. ],
  38. "performanceMetrics": [
  39. {
  40. "metric": "accuracy"
  41. }
  42. ]
  43. },
  44. "expectedOutputs": {
  45. "predictionsFile": "predictions.csv",
  46. "scoresFile": "scores.csv"
  47. }
  48. }

全栈的自动化机器学习系统,主要针对多变量时间序列数据的异常检测。TODS提供了详尽的用于构建基于机器学习的异常检测系统的模块,它们包括:数据处理(data processing),时间序列处理( time series processing),特征分析(feature analysis),检测算法(detection algorithms),和强化模块( reinforcement module)。这些模块所提供的功能包括常见的数据预处理、时间序列数据的平滑或变换,从时域或频域中抽取特征、多种多样的检测算

Contributors (1)