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RapidMiner: Tutorial online + Operadores
Descripción de los Operadores utilizados en el Tutorial de RM5

Descripción de los Operadores utilizados en el Tutorial de RM5

By bernabeu_dario on 18 Noviembre, 2010 - 13:31

Versión para impresión

1. Data Transformation → Aggregation → Aggregate
2. Data Transformation → Attribute Set Reduction and Transformation → Generation → Generate Attributes
3. Data Transformation → Attribute Set Reduction and Transformation → Generation → Generate ID
4. Data Transformation → Attribute Set Reduction and Transformation → Generation → Optimization → Optimize by Generation (YAGGA)
5. Data Transformation → Attribute Set Reduction and Transformation → Principal Component Análisis
6. Data Transformation → Attribute Set Reduction and Transformation → Selection → Optimization → Optimize Selection
7. Data Transformation → Attribute Set Reduction and Transformation → Selection → Optimization → Optimize Selection (Evolutionary)
8. Data Transformation → Attribute Set Reduction and Transformation → Selection → Select Attributes
9. Data Transformation → Attribute Set Reduction and Transformation → Selection → Select by Weights
10. Data Transformation → Attribute Set Reduction and Transformation → Selection → Work on Subset
11. Data Transformation → Attribute Set Reduction and Transformation → Transformation → Singular Value Decomposition
12. Data Transformation → Data Cleansing → Replace Missing Values
13. Data Transformation → Filtering → Filter Examples
14. Data Transformation → Name and Role Modification → Rename
15. Data Transformation → Name and Role Modification → Rename by Replacing
16. Data Transformation → Name and Role Modification → Set Role
17. Data Transformation → Set Operations → Append
18. Data Transformation → Set Operations → Join
19. Data Transformation → Sorting → Sort
20. Data Transformation → Type Conversion → Discretization → Discretize by Frequency
21. Data Transformation → Type Conversion → Discretization → Nominal to Binominal
22. Data Transformation → Value Modification → Numerical Value Modification → Normalize
23. Evaluation → Attributes → Performance (Attribute Count)
24. Evaluation → Attributes → Performance (CFS)
25. Evaluation → Performance Measurement → Classification and Regression → Performance (Binominal Classification)
26. Evaluation → Performance Measurement → Classification and Regression → Performance (Classification)
27. Evaluation → Performance Measurement → Classification and Regression → Performance (Regression)
28. Evaluation → Performance Measurement → Performance
29. Evaluation → Performance Measurement → Performance (Min-Max)
30. Evaluation → Performance Measurement → Performance (User-Based)
31. Evaluation → Significance → ANOVA
32. Evaluation → Significance → T-Test
33. Evaluation → Validation → Split Validation
34. Evaluation → Validation → X-Validation
35. Evaluation → Validation → Wrapper-X-Validation
36. Export → Attributes → Write Constructions
37. Export → Attributes → Write Weights
38. Export → Other → Write Parameters
39. Import → Attributes → Read Constructions
40. Import → Attributes → Read Weights
41. Import → Other → Read Parameters
42. Modeling → Association and Item Set Mining → Create Association Rules
43. Modeling → Association and Item Set Mining → FP-Growth
44. Modeling → Attribute Weighting → Optimization → Optimize Weights (Evolutionary)
45. Modeling → Attribute Weighting → Weight by Chi Squared Statistic
46. Modeling → Classification and Regression → Bayesian Modeling → Naive Bayes
47. Modeling → Classification and Regression → Function Fitting → Linear Regression
48. Modeling → Classification and Regression → Lazy Modeling → k-NN
49. Modeling → Classification and Regression → Meta Modeling → MetaCost
50. Modeling → Classification and Regression → Meta Modeling → Stacking
51. Modeling → Classification and Regression → Support Vector Modeling → Support Vector Machine
52. Modeling → Classification and Regression → Support Vector Modeling → Support Vector Machine (LibSVM)
53. Modeling → Classification and Regression → Tree Induction → Decision Tree
54. Modeling → Clustering and Segmentation → k-Means
55. Modeling → Model Application → Apply Model
56. Modeling → Model Application → Group Models
57. Modeling → Model Application → Thresholds → Apply Threshold
58. Modeling → Model Application → Thresholds → Find Threshold
59. Modeling → Model Application → Ungroup Models
60. Process Control → Branch → Select Subprocess
61. Process Control → Loop → Loop Attributes
62. Process Control → Loop → Loop Values
63. Process Control → Parameter → Optimize Parameters (Grid)
64. Process Control → Multiply
65. Process Control → Parameter → Set Parameters
66. Repository Access → Retrieve
67. Repository Access → Store
68. Utility → Data Generation → Add Noise
69. Utility → Data Generation → Generate Data
70. Utility → Logging → Log
71. Utility → Macros → Extract Macro
72. Utility → Macros → Set Macro
73. Utility → Miscellaneous → Free Memory
74. Utility → Miscellaneous → Materialize Data
75. Utility → Subprocess

Enlaces transversales de libro para Descripción de los Operadores utilizados en el Tutorial de RM5

‹ Conclusión
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1. Data Transformation → Aggregation → Aggregate ›

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Instalación de RapidMiner
Tutorial de RapidMiner 5.0
Descripción de los Operadores utilizados en el Tutorial de RM5
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