DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Data Mining Introduction Getting Started Data Loading Data Exploring Data Interactive Graphics Test Descriptive Data Mining Predictive Data Mining Evaluation and Deployment Data Cleaning Handling Missing Data Transforming Data Data Reduction Deployment Troubleshooting Issues Moving into R Beyond Rattle R Getting Help Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Topics in Data Mining Fraud Analysis Archetype Analysis Text Mining Survival Analysis Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Support Vector Machines Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendices Installing Rattle Bibliography Index Preface Goals Organisation Features Audience Typographical Conventions A Note on Languages Currency Acknowledgements

Using Iris

> library(rpart)
> names(iris)

[1] "Sepal.Length" "Sepal.Width"  "Petal.Length"
[4] "Petal.Width"  "Species"

> iris.rpart <- rpart(Species ~ ., data=iris)
> summary(iris.rpart)

Call:
rpart(formula = Species ~ ., data = iris)
  n= 150 

    CP nsplit rel error xerror      xstd
1 0.50      0      1.00   1.11 0.0537215
2 0.44      1      0.50   0.60 0.0600000
3 0.01      2      0.06   0.11 0.0319270

Node number 1: 150 observations,    complexity param=0.5
  predicted class=setosa      expected loss=0.6666667
    class counts:    50    50    50
   probabilities: 0.333 0.333 0.333 
  left son=2 (50 obs) right son=3 (100 obs)
  Primary splits:
      Petal.Length < 2.45 to the left,  improve=50.00000, (0 missing)
      Petal.Width  < 0.8  to the left,  improve=50.00000, (0 missing)
      Sepal.Length < 5.45 to the left,  improve=34.16405, (0 missing)
      Sepal.Width  < 3.35 to the right, improve=19.03851, (0 missing)
  Surrogate splits:
      Petal.Width  < 0.8  to the left,  agree=1.000, adj=1.00, (0 split)
      Sepal.Length < 5.45 to the left,  agree=0.920, adj=0.76, (0 split)
      Sepal.Width  < 3.35 to the right, agree=0.833, adj=0.50, (0 split)

Node number 2: 50 observations
  predicted class=setosa      expected loss=0
    class counts:    50     0     0
   probabilities: 1.000 0.000 0.000 

Node number 3: 100 observations,    complexity param=0.44
  predicted class=versicolor  expected loss=0.5
    class counts:     0    50    50
   probabilities: 0.000 0.500 0.500 
  left son=6 (54 obs) right son=7 (46 obs)
  Primary splits:
      Petal.Width  < 1.75 to the left,  improve=38.969400, (0 missing)
      Petal.Length < 4.75 to the left,  improve=37.353540, (0 missing)
      Sepal.Length < 6.15 to the left,  improve=10.686870, (0 missing)
      Sepal.Width  < 2.45 to the left,  improve= 3.555556, (0 missing)
  Surrogate splits:
      Petal.Length < 4.75 to the left,  agree=0.91, adj=0.804, (0 split)
      Sepal.Length < 6.15 to the left,  agree=0.73, adj=0.413, (0 split)
      Sepal.Width  < 2.95 to the left,  agree=0.67, adj=0.283, (0 split)

Node number 6: 54 observations
  predicted class=versicolor  expected loss=0.09259259
    class counts:     0    49     5
   probabilities: 0.000 0.907 0.093 

Node number 7: 46 observations
  predicted class=virginica   expected loss=0.02173913
    class counts:     0     1    45
   probabilities: 0.000 0.022 0.978

Support further development through the purchase of the PDF version of the book.
The PDF version is a formatted comprehensive draft book (with over 800 pages).
Brought to you by Togaware. This page generated: Saturday, 16 January 2010