ce-calibrated-predict
Produce calibrated predict and predict_proba outputs, with optional uncertainty intervals, without generating explanations.
npx skills add majiayu000/claude-skill-registry --skill ce-calibrated-predict --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# CE Calibrated Predict You are obtaining calibrated predictions **without explanations** from a CE explainer. This is the lightest entry point — no explanation rules are generated, just the calibrated prediction and (optionally) its uncertainty interval. A fully fit + calibrated `WrapCalibratedExplainer` is required. --- ## Quick reference | Method | Returns | Use for | |---|---|---| | `predict(x)` | Point prediction (class or y-value) | Classification and regression | | `predict(x, uq_interval=True)` | `(prediction, (low, high))` | Point pred + uncertainty bounds | | `predict_proba(x)` | Class probability array | Classification | | `predict_proba(x, uq_interval=True)` | `(proba, (low, high))` | Probability + bounds | | `predict_proba(x, threshold=t)` | `P(y ≤ t)` | Probabilistic regression | | `predict_proba(x, threshold=t, uq_interval=True)` | `(P(y ≤ t), (low, high))` | Regression with probability bounds | --- ## Classification — `predict` ```python # Point prediction (returns class labels for classification) y_hat = explainer.predict(x_test) # With uncertainty interval (returns tuple) y_hat, (low, high) = explainer.predict(x_test, uq_interval=True) # Interpretation: low ≤ p(cl
- Quick reference
- Classification — predict
- Classification — predictproba
- Regression — predict
- Regression — predictproba (thresholded)
- Conditional predictions (Mondrian bins)
- Reject-aware predictions
- Uncalibrated model — expected warnings
- Choosing between predict and explainfactual
- Evaluation Checklist
What does the ce-calibrated-predict skill do?
Produce calibrated predict and predict_proba outputs, with optional uncertainty intervals, without generating explanations.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ce-calibrated-predict --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.