Use case 01
You already have a candidate model worth tuning seriously.
Data ScientistModel BuildingIntermediateSingle prompt
Hyperparameter Tuning AI Prompt
This prompt runs systematic hyperparameter optimization instead of manual guesswork. It is most useful after a promising model family has been identified and you want measurable gains from tuning. The workflow emphasizes Bayesian search, reproducibility, and comparison to defaults.
Prompt text
Tune the hyperparameters of this model to maximize performance on {{target_variable}}.
Model to tune: {{model_type}} (e.g. LightGBM, XGBoost, Random Forest)
Approach:
1. Define the hyperparameter search space:
- For tree models: n_estimators, max_depth, learning_rate, min_child_samples, subsample, colsample_bytree, reg_alpha, reg_lambda
- For linear models: C, penalty, solver
2. Use Optuna (Bayesian optimization) with 100 trials
3. Evaluate each trial with 5-fold cross-validation
4. Plot the optimization history: score vs trial number
5. Report the best hyperparameters and best cross-validated score
6. Compare: default params vs tuned params — how much did tuning improve performance?
Return: best params dict, improvement table, and training code using the best params.When to use this prompt
Use case 02
Manual parameter tweaking is too slow or inconsistent.
Use case 03
You want Optuna-based search with cross-validation and clear reporting.
Use case 04
You need proof that tuning improved performance versus defaults.
What the AI should return
The best parameter set, optimization history summary, comparison of tuned versus default performance, and clean training code using the selected hyperparameters.
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 Model Building.
Frequently asked questions
What does the Hyperparameter Tuning prompt do?+
It gives you a structured model building 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?+
Hyperparameter Tuning 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 AutoML Benchmark, Baseline Model, Class Imbalance Handling.