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

[Task Information of Decision Tree]

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.

[Task Information of Random Forests]

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.

[Task Information of Decision Tree(C5.0)

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

https://cran.r-project.org/web/packages/C50/index.html