Use case 01
The production model is too complex for direct explanation.
Data ScientistExplainabilityBeginnerSingle prompt
Decision Tree Proxy AI Prompt
This prompt builds a shallow decision tree that approximates a complex model for interpretability. It is useful when the real model is hard to explain but stakeholders still want simplified rules of thumb. The key idea is fidelity: the tree should mimic the complex model as well as possible while staying readable.
Prompt text
Build a simple decision tree that approximates the behavior of this complex model. 1. Generate predictions from the complex model on the full training set 2. Train a decision tree on those predictions (use model outputs as the new target) 3. Limit the tree depth to 4 levels maximum for interpretability 4. Tune: find the depth (1–6) that maximizes fidelity (agreement with the complex model) while staying interpretable 5. Visualize the decision tree using graphviz or a text representation 6. Extract the top 5 decision rules as plain-English if-then statements 7. Report fidelity: what percentage of predictions does the proxy tree agree with the complex model? Note: this is a surrogate model, not the real model. Flag where the proxy disagrees most with the original.
When to use this prompt
Use case 02
Stakeholders want simple if-then rules approximating model behavior.
Use case 03
You need a surrogate explanation with measurable fidelity.
Use case 04
You want to know where the simplified proxy breaks down.
What the AI should return
A tuned shallow surrogate tree, fidelity score, key extracted rules in plain English, visualization or text representation of the tree, and notes on where the proxy disagrees with the original model.
How to use this prompt
1
Open your data context
Load your dataset, notebook, or working environment so the AI can operate on the actual project context.
2
Copy the prompt text
Use the copy button above and paste the prompt into the AI assistant or prompt input area.
3
Review the output critically
Check whether the result matches your data, assumptions, and desired format before moving on.
4
Chain into the next prompt
Once you have the first result, continue deeper with related prompts in Explainability.
Frequently asked questions
What does the Decision Tree Proxy prompt do?+
It gives you a structured explainability starting point for data scientist work and helps you move faster without starting from a blank page.
Who is this prompt for?+
It is designed for data scientist workflows and marked as beginner, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Decision Tree Proxy is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.
Can I use this outside MLJAR Studio?+
Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.
What should I open next?+
Natural next steps from here are Counterfactual Explanations, Feature Importance, Full XAI Chain.