갱신 : 2021-03-17
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갱신 : 2020-09-21
목차
I. 시작하기 전에
1. 저작권 안내
2. 일러두기
II. 제품 개요
1. i-STREAM 소개
2. i-STREAM 화면구성
3. 기본 사용방법
4. 워크플로우 작성
III. i-STREAM 기능 및 사용
1. DataIO
A01. InSql
A02. OutFile
A03. InFile
A04. CRUD
A05. i-META Viewer
2. PreProcessing
B01. AddDerCols
B02. DropCol
B03. RenameCols
B04. ReorderCol
B05. SortData
B06. FilterData
B07. FilterData2
B08. FillVal
B09. FillNull
B10. FillNullS
B11. Normalizing
B12. Melting
B13. Casting
B14. JoinData
B15. UnionData
B16. Ranking
B17. NumBering
B18. Aggregate
3. Analytics
C01. Ctree
C02. Ctree2
C03. CfRec
C04. ItemRec
C05. NBayes
C06. NBayes2
C07. RdmForest
C08. DiffModel
C09. AnnClass
C10. AnnReg
C11. ClassifyCom
C12. ClsfScore
C13. KMeans
C14. HClustering
C15. Apriori
C16. Arima
C17. AutoArima
C18. ESmooth
C19. HoltWinter
C20. MA
C21. WMA
C22. STL
C23. ForeComp
C24. LinearReg
C25. LogitReg
C26. RScript
C27. IPGCC (IV, PSI, GINI, Chi-square, Correlation)
C28. CoarseBinning
C29. K-S Test
C30. K-S Class
C31. GLogitR (Generalized Logistic Regression)
C32. GLR (Generalized Linear Regression)
C33. C5.0 (Decision Tree)
C34. RPART (RP&RT - Decision Tree)
C35. ClsfDRF (Distributed Random Forest)
C36. LinearDRF (Distributed Random Forest)
C37. ClsfGBM (Gradient Boost Modeling)
C38. LinearGBM (Gradient Boost Modeling)
C39. ClsfSVM
C40. LinearSVM
C41. KNN
C42. PCA
C43. DBSCAN(Density-Based Spatial Clustering of Applications with Noise)
C44. ClsfDNN (Deep Learning)
C45. LinearDNN (Deep Learning)
4. Visualization
D01. HISTOGRAM
D02. BOXPLOT
D03. SCATTER
5. Controls
E01. Comment
E02. VARIABLE
E03. WORKFLOW
E04. IfFail
E05. ByLoop
E06. Python
E07. IPython(Interactive Python)
6. 관리 기능
F01. 스케쥴러
F02. time_unit
IV. FAQ
1. 제품/설치 관련
2. 세부 기능 관련