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datasetDoc.json 4.4 kB

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5 years ago
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  1. {
  2. "about": {
  3. "datasetID": "template",
  4. "datasetName": "baseball",
  5. "description": "Database of baseball players and play statistics, including 'Games_played', 'At_bats', 'Runs', 'Hits', 'Doubles', 'Triples', 'Home_runs', 'RBIs', 'Walks', 'Strikeouts', 'Batting_average', 'On_base_pct', 'Slugging_pct' and 'Fielding_ave'",
  6. "citation": " @book{simonoff2003analyzing,title={Analyzing Categorical Data},author={Simonoff, J.S.},isbn={9780387007496},lccn={2003044946},series={Springer Texts in Statistics},url={https://books.google.com/books?id=G8wrifweAoC},year={2003},publisher={Springer New York}} ",
  7. "license": " CC Public Domain Mark 1.0 ",
  8. "source": "OpenML",
  9. "sourceURI": "http://www.openml.org/d/185",
  10. "approximateSize": "",
  11. "datasetSchemaVersion": "4.0.0",
  12. "redacted": false,
  13. "datasetVersion": "4.0.0"
  14. },
  15. "dataResources": [
  16. {
  17. "resID": "learningData",
  18. "resPath": "tables/learningData.csv",
  19. "resType": "table",
  20. "resFormat": {
  21. "text/csv": [
  22. "csv"
  23. ]
  24. },
  25. "isCollection": false,
  26. "columns": [
  27. {
  28. "colIndex": 0,
  29. "colName": "d3mIndex",
  30. "colType": "integer",
  31. "role": [
  32. "index"
  33. ]
  34. },
  35. {
  36. "colIndex": 1,
  37. "colName": "Player",
  38. "colType": "categorical",
  39. "role": [
  40. "attribute"
  41. ]
  42. },
  43. {
  44. "colIndex": 2,
  45. "colName": "Number_seasons",
  46. "colType": "integer",
  47. "role": [
  48. "attribute"
  49. ]
  50. },
  51. {
  52. "colIndex": 3,
  53. "colName": "Games_played",
  54. "colType": "integer",
  55. "role": [
  56. "attribute"
  57. ]
  58. },
  59. {
  60. "colIndex": 4,
  61. "colName": "At_bats",
  62. "colType": "integer",
  63. "role": [
  64. "attribute"
  65. ]
  66. },
  67. {
  68. "colIndex": 5,
  69. "colName": "Runs",
  70. "colType": "integer",
  71. "role": [
  72. "attribute"
  73. ]
  74. },
  75. {
  76. "colIndex": 6,
  77. "colName": "Hits",
  78. "colType": "integer",
  79. "role": [
  80. "attribute"
  81. ]
  82. },
  83. {
  84. "colIndex": 7,
  85. "colName": "Doubles",
  86. "colType": "integer",
  87. "role": [
  88. "attribute"
  89. ]
  90. },
  91. {
  92. "colIndex": 8,
  93. "colName": "Triples",
  94. "colType": "integer",
  95. "role": [
  96. "attribute"
  97. ]
  98. },
  99. {
  100. "colIndex": 9,
  101. "colName": "Home_runs",
  102. "colType": "integer",
  103. "role": [
  104. "attribute"
  105. ]
  106. },
  107. {
  108. "colIndex": 10,
  109. "colName": "RBIs",
  110. "colType": "integer",
  111. "role": [
  112. "attribute"
  113. ]
  114. },
  115. {
  116. "colIndex": 11,
  117. "colName": "Walks",
  118. "colType": "integer",
  119. "role": [
  120. "attribute"
  121. ]
  122. },
  123. {
  124. "colIndex": 12,
  125. "colName": "Strikeouts",
  126. "colType": "integer",
  127. "role": [
  128. "attribute"
  129. ]
  130. },
  131. {
  132. "colIndex": 13,
  133. "colName": "Batting_average",
  134. "colType": "real",
  135. "role": [
  136. "attribute"
  137. ]
  138. },
  139. {
  140. "colIndex": 14,
  141. "colName": "On_base_pct",
  142. "colType": "real",
  143. "role": [
  144. "attribute"
  145. ]
  146. },
  147. {
  148. "colIndex": 15,
  149. "colName": "Slugging_pct",
  150. "colType": "real",
  151. "role": [
  152. "attribute"
  153. ]
  154. },
  155. {
  156. "colIndex": 16,
  157. "colName": "Fielding_ave",
  158. "colType": "real",
  159. "role": [
  160. "attribute"
  161. ]
  162. },
  163. {
  164. "colIndex": 17,
  165. "colName": "Position",
  166. "colType": "categorical",
  167. "role": [
  168. "attribute"
  169. ]
  170. },
  171. {
  172. "colIndex": 18,
  173. "colName": "Hall_of_Fame",
  174. "colType": "categorical",
  175. "role": [
  176. "suggestedTarget"
  177. ]
  178. }
  179. ],
  180. "columnsCount": 19
  181. }
  182. ]
  183. }

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