Decision Tree - only one attribute per branch?

DBenkert

Hello, i hope everyone here is doing great!

I have a question regarding decision trees. Is it possible to set up the decision tree in a way, so that the model will use every attribute just once per branch? I need this for a project for my studies, and it would mean a lot if someone here can help me .

Thanks in advance!

MartinLiebig

Hi,

not with the normal decision trees. The interactive ones may be grown like this.

BR,

Martin

machbrown2

Hello,

Certainly! In a One Rule decision tree:

1. Select Best Attribute:

   • Evaluate each attribute's classification error.
   • Choose the attribute with the lowest error.

2. Create Rule:

   • Formulate a rule based on the selected attribute.

3. Apply Rule:

   • Classify instances based on the rule.

4. Repeat for Each Attribute:

   • Iterate through all attributes to find the best rule.

5. Choose Best Rule:

   • Select the rule that performs best on the data.

6. Tree Structure:

   • The decision tree has a single level, with branches corresponding to attributes and leaf nodes representing predicted classes.

Example in Python (using scikit-learn):

<p>from sklearn.tree import DecisionTreeClassifier, export_text</p><p><br></p><p># Create a One Rule decision tree</p><p>tree_clf = DecisionTreeClassifier(max_depth=1)</p><p><br></p><p># Train the classifier</p><p>tree_clf.fit(X_train, y_train)</p><p><br></p><p># Display the selected rule</p><p>tree_rules = export_text(tree_clf, feature_names=iris.feature_names)</p><p>print(tree_rules)</p>

DBenkert

Ahh thank you so much!!! i really appreciate your help!