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The decision tree model has been working for a long time.

I run my decision tree model using the rpart package in R. Here's what I do,

  • Downloading my data with read.csv
  • Delete unwanted columns
  • Share my dataset for training and testing
  • Installing my model on a training kit - It works all day.

Here is a summary of my dataset.

'data.frame':   117919 obs. of  7 variables:
 $ Database          : Factor w/ 2 levels "DBIL","DBPD": 1 1 1 1 1 1 1 1 1 1 ...
 $ Market_Description: Factor w/ 1 level "MY (PM)": 1 1 1 1 1 1 1 1 1 1 ...
 $ Manufacturer      : Factor w/ 21 levels "21 Century","Abbott Lab",..: 4 3 4 4 4 4 3 3 3 3 ...
 $ Brand             : Factor w/ 133 levels "","21 Century",..: 34 26 34 34 34 34 26 26 26 26 ...
 $ Sub_Brand         : Factor w/ 194 levels "","0-6 Bulan",..: 9 6 9 9 9 9 6 6 6 6 ...
 $ Age_Group         : Factor w/ 5 levels "","Adultenr",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ FMT_Category      : Factor w/ 10 levels "Adult Powders (excl Super Bev)",..: 5 5 5 5 5 5 5 5 5 5 ...

Here is my script for the model.

fit <- rpart(FMT_Category~Database+Market_Description+Manufacturer+Brand+Sub_Brand+Age_Group, data=trainingset)

117919 . memory.limit R, 8065, mem_used 40 . , . , . , R - , . , - , stringAsFactors = FALSE. . python script weka, , . , , , , .

. , Sub_Brand, , . ?

+4
3
  • Sub_Brand .
  • Market_Description, 1 .
+1

H2O package , . . h2o.gbm, h2o.randomForest. .

:

library(h2o)
conn <- h2o.init()
demo(h2o.randomForest)
0

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