README.md

In ntels-BI/ntelsBI: Analysis R package for ntels Business Intelligence Team

Install

devtools::install_github("ntels-BI/ntelsBI")

Tutorial

Machine learning

ml() 함수를 이용하여 기계학습 훈련 및 평가를 수행

• CART 를 통한 iris 꽃잎분류

ml(iris, "Species", method = "rpart") %>% fitSummary(type = "cla")

## Confusion Matrix and Statistics
## 
##             Reference
## Prediction   setosa versicolor virginica
##   setosa         15          0         0
##   versicolor      0         12         1
##   virginica       0          3        14
## 
## Overall Statistics
##                                           
##                Accuracy : 0.9111          
##                  95% CI : (0.7878, 0.9752)
##     No Information Rate : 0.3333          
##     P-Value [Acc > NIR] : 8.467e-16       
##                                           
##                   Kappa : 0.8667          
##  Mcnemar's Test P-Value : NA              
## 
## Statistics by Class:
## 
##                      Class: setosa Class: versicolor Class: virginica
## Sensitivity                 1.0000            0.8000           0.9333
## Specificity                 1.0000            0.9667           0.9000
## Pos Pred Value              1.0000            0.9231           0.8235
## Neg Pred Value              1.0000            0.9062           0.9643
## Prevalence                  0.3333            0.3333           0.3333
## Detection Rate              0.3333            0.2667           0.3111
## Detection Prevalence        0.3333            0.2889           0.3778
## Balanced Accuracy           1.0000            0.8833           0.9167

• GLM 을 통한 Sonar 의 Class 분류 (Class = "R" 이 주관심사일 경우)

data(Sonar, package = "mlbench")
ml(Sonar, "Class", method = "glm") %>% fitSummary(type = "cla", positive = "R")

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction  M  R
##          M 22  9
##          R 11 20
##                                           
##                Accuracy : 0.6774          
##                  95% CI : (0.5466, 0.7906)
##     No Information Rate : 0.5323          
##     P-Value [Acc > NIR] : 0.01444         
##                                           
##                   Kappa : 0.3548          
##  Mcnemar's Test P-Value : 0.82306         
##                                           
##             Sensitivity : 0.6897          
##             Specificity : 0.6667          
##          Pos Pred Value : 0.6452          
##          Neg Pred Value : 0.7097          
##              Prevalence : 0.4677          
##          Detection Rate : 0.3226          
##    Detection Prevalence : 0.5000          
##       Balanced Accuracy : 0.6782          
##                                           
##        'Positive' Class : R               
##

• Random forest 를 통한 mtcars mpg 예측

ml(mtcars, "mpg", method = "rf") %>% fitSummary(type = "reg")

## [1] 10.91898

Useful Preprocessing

• spCoordinateTrans() function : 위경도 좌표변환

exdata <- data.frame(long = c(1039197, 1041137, 1039216, 1037176), 
                     lat = c(1926417, 1927056, 1927526, 1924963))

exdata # UTM-K(GRS-80)

##      long     lat
## 1 1039197 1926417
## 2 1041137 1927056
## 3 1039216 1927526
## 4 1037176 1924963

spCoordinateTrans(exdata$long, exdata$lat) # To WGS84

##       long      lat
## 1 127.9425 37.33594
## 2 127.9644 37.34161
## 3 127.9428 37.34593
## 4 127.9196 37.32292

License

GPL-3

ntels-BI/ntelsBI documentation built on May 24, 2019, 2:49 p.m.