Information based ML
Decision Tree (cart), Random Forests, Decision Tree (C5.0)
Decision Tree(cart)
Input Data
*Target Data
Input Data
*Model Name
Name of the model
Output Name
Name of the output after the calculation(optional)
Merge Data
If not empty, the prediction data will be stored in the selected data frame(optional)
*X
Independent variables
*Y
Dependent variable
Random Forests
Input Data
*Target Data
Input Data
*Model Name
Name of the model
Output Name
Name of the output after the calculation(optional)
Merge Data
If not empty, the prediction data will be stored in the selected data frame(optional)
X
Independent variables
Y
Dependent variable.
If the variable for Y is not a factor, convert it by using Convert Factor before running the model.
Decision Tree (C5.0)
Input Data
*Target Data
Input Data
*Model Name
Name of the model
Output Name
Name of the output after the calculation(optional)
Merge Data
If not empty, the prediction data will be stored in the selected data frame(optional)
X
Independent variables
Y
Dependent variable. Factor type only
If the variable for Y is not a factor, convert it by using Convert Factor before running the model.
Workflow Example
R Packages
Decision Tree (cart)
Package name: rpart, Method: rpart
https://cran.r-project.org/web/packages/rpart/index.html
Random Forests
Package name: randomForest, Method: randomForest
https://cran.r-project.org/web/packages/randomForest/index.html
Decision Tree (C5.0)
Package name: C50, Method: C5.0.default
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