【独家发布】【kindle】R Data Analysis Cookbook - More Than 80 Recipes to Help You Delive

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感谢楼主提供好资料！

看来是一本很好的R工具书。

谢谢楼主分享啊

R软件很不错，打算学习

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Aprender todos los días！

【独家发布】【kindle】R Data Analysis Cookbook - More Than 80 Recipes to Help You Delive
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1. Generating error/classification-confusion
   
2. matrices
   
3. > cp <- read.csv("college-perf.csv")
   
4. > cp$Perf <- ordered(cp$Perf, levels =
   
5. + c("Low", "Medium", "High"))
   
6. > cp$Pred <- ordered(cp$Pred, levels =
   
7. + c("Low", "Medium", "High"))
   
8. 
   
9. 1. First create and display a two-way table based on the actual and predicted values:
   
10. > tab <- table(cp$Perf, cp$Pred,
    
11. + dnn = c("Actual", "Predicted"))
    
12. > tab
    
13. 
    
14. 2. Display the raw numbers as proportions or percentages. To get overall table-level
    
15. proportions use:
    
16. > prop.table(tab)
    
17. 
    
18. 3. We often find it more convenient to interpret row-wise or column-wise percentages.
    
19. To get row-wise percentages rounded to one decimal place, you can pass a second
    
20. argument as 1:
    
21. > round(prop.table(tab, 1)*100, 1)

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1. Generating ROC charts
   
2. To generate ROC charts, follow these steps:
   
3. 1. Load the package ROCR:
   
4. > library(ROCR)
   
5. 2. Read the data file and take a look:
   
6. > dat <- read.csv("roc-example-1.csv")
   
7. > head(dat)
   
8. 3. Create the prediction object:
   
9. > pred <- prediction(dat$prob, dat$class)
   
10. 4. Create the performance object:
    
11. > perf <- performance(pred, "tpr", "fpr")
    
12. 5. Plot the chart:
    
13. > plot(perf)
    
14. > lines( par()$usr[1:2], par()$usr[3:4] )

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