Calculator Inputs
This calculator estimates a PPS-style score from baseline, model, and validation performance. It is transparent and practical for quick model reviews.
Example Data Table
| Predictor | Target | Baseline | Model | Cross Validation | Estimated PPS | Interpretation |
|---|---|---|---|---|---|---|
| Marketing Spend | Lead Conversion | 52% | 78% | 74% | 0.48 | Moderate |
| Session Duration | Purchase Intent | 44% | 69% | 66% | 0.39 | Weak to Moderate |
| Support Tickets | Churn Risk | 50% | 83% | 81% | 0.59 | Moderate |
| App Events | Retention | 47% | 76% | 73% | 0.46 | Moderate |
Formula Used
This page uses a transparent PPS-style estimator. Official PPS libraries often train internal models directly from raw data. This calculator instead estimates predictive strength from evaluation metrics you already know.
For higher-is-better metrics: Raw PPS = max(0, (Model Score - Baseline Score) / (1 - Baseline Score)) For lower-is-better metrics: Raw PPS = max(0, (Baseline Error - Model Error) / Baseline Error) Stability = 1 - |Model Score - CV Score| / max(|Model Score|, |CV Score|, ε) Sample Factor = min(1, log10(Sample Size + 1) / 4) Data Quality = 1 - Missing Data % Holdout Factor = clamp(Test Split % / 20, 0.50, 1) Penalty Factor = (1 - Leakage Penalty) × (1 - Complexity Penalty) Adjusted PPS = Raw PPS × Stability × Sample Factor × Data Quality × Holdout Factor × Penalty Factor
All final results are bounded between 0 and 1. Higher values indicate stronger predictive usefulness after validation and quality adjustments.
How to Use This Calculator
- Enter the predictor field and the target field names.
- Choose whether your evaluation metric improves when it rises or falls.
- Select the scale that matches your scores or errors.
- Enter baseline, model, and cross validation values from the same metric family.
- Provide sample size, test split, missing rate, and penalty assumptions.
- Press calculate to view the adjusted PPS, confidence, and graph.
- Use the export buttons to save the results as CSV or PDF.
FAQs
1. What does Predictive Power Score mean?
It estimates how strongly one variable can predict another. A value near zero means weak usefulness. A value near one means strong practical predictive value.
2. Is this the exact same as the PPS package?
No. This version is a transparent PPS-style estimator built from known evaluation metrics. It is useful for planning, audits, and quick comparisons when raw modeling pipelines are unavailable.
3. Why include a baseline score?
Predictive strength should reflect improvement over a naive benchmark. Without a baseline, a model score alone cannot show whether the predictor truly adds useful signal.
4. Why does cross validation affect the result?
A feature can look powerful on one split and fail elsewhere. The stability factor rewards agreement between holdout performance and cross validation performance.
5. When should I use lower-is-better mode?
Use it for error metrics like MAE, RMSE, MAPE, or log loss. In those cases, smaller values indicate stronger prediction quality.
6. What do the penalties represent?
Leakage penalty reduces inflated results caused by future information or target leakage. Complexity penalty reduces overly optimistic scores from fragile or excessively tuned models.
7. How should I interpret a weak score?
A weak score does not always mean useless data. It may mean poor validation, too little data, noisy labels, or limited incremental gain over the baseline.
8. Can I compare several predictors with this page?
Yes. Run the calculator separately for each predictor using the same target, metric family, data split logic, and penalty assumptions for fair comparison.