Use case 01
You are starting a modeling project and need credible baselines.
Data ScientistModel BuildingBeginnerSingle prompt
Baseline Model AI Prompt
This prompt establishes honest baseline performance before more complex modeling begins. It is useful because many projects jump straight to sophisticated algorithms without proving that they beat trivial or simple alternatives. The prompt helps define the minimum bar a useful model must clear.
Prompt text
Build baseline models for predicting {{target_variable}} in this dataset.
1. Determine the problem type: binary classification, multiclass classification, or regression
2. Choose the correct evaluation metric: AUC-ROC for binary, accuracy/F1 for multiclass, RMSE/MAE for regression
3. Build a naive baseline first:
- Regression: predict the training set mean for all observations
- Classification: predict the majority class for all observations
4. Build two simple baselines: Logistic Regression (or Linear Regression) and a Decision Tree with max_depth=3
5. Evaluate all three on a held-out validation set (20% split, stratified for classification)
Return a comparison table: model | train score | validation score | fit time
Identify which baseline to beat before calling any model 'useful'.When to use this prompt
Use case 02
You want to verify the problem type and evaluation metric first.
Use case 03
You need a naive benchmark and a couple of simple ML baselines.
Use case 04
You want a clean table showing what future models must outperform.
What the AI should return
A comparison table for naive and simple baseline models including train score, validation score, and fit time, plus a short recommendation on the baseline threshold any serious model should beat.
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 Baseline Model 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 beginner, so it works well as a guided starting point for that level of experience.
What type of prompt is this?+
Baseline Model 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, Class Imbalance Handling, Custom Loss Function.