Use case 01
You want to reduce feature count without sacrificing much performance.
Data ScientistFeature EngineeringIntermediateSingle prompt
Feature Selection AI Prompt
This prompt compares several feature selection philosophies to identify a smaller and more robust predictor set. It is useful when the raw feature space is large or when you need stability rather than one lucky importance ranking. The final recommendation rewards agreement across multiple methods.
Prompt text
Select the optimal feature subset for predicting {{target_variable}}.
Run four feature selection methods and compare their results:
1. Filter method: correlation with target (keep features with |r| > 0.05)
2. Wrapper method: Recursive Feature Elimination (RFE) with a Random Forest estimator, 5-fold CV
3. Embedded method: SHAP values from a LightGBM model — keep top features by mean |SHAP|
4. Stability method: run SHAP selection 5 times with different random seeds — keep only features that appear in all 5 runs (stable features)
Compare: how many features does each method select? How much do the selected sets overlap?
Final recommendation: the intersection of features selected by at least 3 of the 4 methods.
Return: selected feature list, overlap Venn diagram, and CV performance with all features vs selected features.When to use this prompt
Use case 02
The current feature space may be noisy, redundant, or unstable.
Use case 03
You want to compare filter, wrapper, embedded, and stability methods.
Use case 04
You need evidence that the selected subset generalizes well.
What the AI should return
Selected feature sets from each method, overlap analysis, a final recommended subset, and performance comparison of using all features versus the chosen reduced set.
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 Feature Engineering.
Frequently asked questions
What does the Feature Selection prompt do?+
It gives you a structured feature engineering 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 intermediate, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Feature Selection 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 Date Feature Extraction, Embedding Features from Text, Feature Ideas Generator.