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kI'm trying to build a decision tree model on a dataset that only has categorical values, an example fragment of the dataset is below. My training dataset consists of 40 observations

  Repository   PrimaryLanguage   BuildTools  IntegrationRequired  Active
1 repo-name-1  Java              Maven        yes                 yes
2 repo-name-2  Python            setuptools   yes                 yes
3 repo-name-3  YAML              None         no                  yes
4 repo-name-4  Java              Gradle       yes                 yes
5 repo-name-5  Shell             None         no                  no

The goal is to determine if a repo requires integration based on what type of language, and build tools it uses or if it is even active.

r code to build the tree model -

ml <- rpart(IntegrationRequired~PrimaryLanguage + BuildTools + Active, data=train, control = rpart.control("minsplit" = 2), method='class')

But my tree generates very few branches, and it splits nodes based on entire column instead of values in that column. How do I fix this or I should look at other algorithms like kmeans? Any help is deeply appreciated.

Plot - enter image description here

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  • $\begingroup$ You don't have much data, so there's a limit to what any algorithm can achieve. $\endgroup$
    – mkt
    Sep 3, 2022 at 8:59
  • $\begingroup$ "It splits nodes based on entire column instead of values in that column" This and related threads may be useful: stats.stackexchange.com/questions/534680/… $\endgroup$
    – mkt
    Sep 3, 2022 at 9:03
  • $\begingroup$ First, why post a table of example data instead of a reproducible example? Second, I see a reasonable tree as result and am not sure, what your desired output would look like. This tree classifies to 3% out of 40 cases with 1/40 = 0.025 = 3% so why would you want more branches? $\endgroup$
    – Bernhard
    Sep 3, 2022 at 9:13
  • $\begingroup$ I don't think the tree is using the 'Active' category to make the decision. My objective is to create something like this - towardsdatascience.com/…, maybe I need to take a better look at Gini index, I'll post the solution if I get it working, thanks $\endgroup$
    – Milap Jhumkhawala
    Sep 3, 2022 at 22:42

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