CSU-Carig
Cichosz, Pawel,
Data mining algorithms : explained using R / - 683 pages ; illustration ;
Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naèive Bayes classifier -- 5. Linear classification -- 6. Misclassification costs -- 7. Classification model evaluation -- Part III. Regression -- 8. Linear regression -- 9. Regression trees -- 10. Regression model evaluation -- Part IV. Clustering -- 11. (Dis)similarity measures -- 12. k-Centers clustering -- 13. Hierarchical clustering -- 14. Clustering model evaluation -- Part V. Getting better models -- 15. Model ensembles -- 16. Kernel methods -- 17. Attribute transformation -- 18. Discretization -- 19. Attribute selection.
"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--
9781118332580
Data mining.
Computer algorithms.
R (Computer program language)
MATHEMATICS / Probability & Statistics / General.
006.312
Data mining algorithms : explained using R / - 683 pages ; illustration ;
Part I. Preliminaries -- 1. Tasks -- 2. Basic statistics -- Part II. Classification -- 3. Decision trees -- 4. Naèive Bayes classifier -- 5. Linear classification -- 6. Misclassification costs -- 7. Classification model evaluation -- Part III. Regression -- 8. Linear regression -- 9. Regression trees -- 10. Regression model evaluation -- Part IV. Clustering -- 11. (Dis)similarity measures -- 12. k-Centers clustering -- 13. Hierarchical clustering -- 14. Clustering model evaluation -- Part V. Getting better models -- 15. Model ensembles -- 16. Kernel methods -- 17. Attribute transformation -- 18. Discretization -- 19. Attribute selection.
"This book narrows down the scope of data mining by adopting a heavily modeling-oriented perspective"--
9781118332580
Data mining.
Computer algorithms.
R (Computer program language)
MATHEMATICS / Probability & Statistics / General.
006.312