Baseline First Modeling
- 1 installs
- Updated May 22, 2026
- creyesp/datapowers
Build ML models following baseline-first methodology with validation strategies declared upfront.
About
Datapowers Baseline-First Modeling enforces Iron Laws: establish baseline and validation strategy before training. Covers framing, data, EDA, features, and monitoring.
- No model training without baseline declared
- 30 skills for full ML lifecycle
Baseline First Modeling by the numbers
- 1 all-time installs (skills.sh)
- Ranked #1,803 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Jul 8, 2026 (Skillselion catalog sync)
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| Installs | 1 |
|---|---|
| Last updated | May 22, 2026 |
| Repository | creyesp/datapowers ↗ |
What it does
Build ML models following baseline-first methodology with validation strategies declared upfront.
Files
Baseline-First Modeling
Overview
A baseline is the floor every other model must beat. Without one, "85% accuracy" is a number, not a result.
Core principle: If a trivial baseline already hits the deploy threshold, you don't need a model — you need a rule.
The Iron Law
NO COMPLEX MODEL (XGBoost, deep net, fine-tune) WITHOUT A DOCUMENTED BASELINE FIRSTThe baseline lives in code, in the experiment spec §5, and as a tracked MLflow run.
When to Use
- Start of every modeling phase.
- Before tuning anything.
- After a major data refresh (re-baseline).
- When stakeholder asks "is this good?" — only the baseline lets you answer.
Baselines by Problem Type
| Problem | Trivial baseline | Strong baseline |
|---|---|---|
| Binary classification | Majority class | Logistic regression on numeric features |
| Multi-class | Class prior | Logistic / linear SVM |
| Regression | Mean / median | Ridge regression on numeric features |
| Time-series forecast | Last value, seasonal naive | Exponential smoothing / ARIMA |
| Ranking / recsys | Popularity, recency | BPR / ALS / lightFM |
| NLP classification | Class prior | TF-IDF + logistic regression |
| NLP generation | Retrieve nearest training example | Small fine-tune of base model |
| Vision classification | Class prior | Linear probe on a pretrained backbone |
| Anomaly detection | Rate threshold on a single feature | IsolationForest |
The Process
Step 1: Implement the trivial baseline
- 10 lines or less.
- Tracked in MLflow.
- Score noted in spec §5.
Step 2: Implement the strong baseline
- Use the same validation strategy that the future complex model will use.
- Same data hash, same pre-processing.
- Tracked in MLflow with confidence intervals.
Step 3: Decide
- Strong baseline beats deploy threshold? → Stop. Ship the baseline. Open a ticket for re-evaluation if context changes.
- Strong baseline misses threshold? → Authorize complex modeling. The complex model must beat the strong baseline by a margin > CI to count.
Pattern
from sklearn.dummy import DummyClassifier
from sklearn.linear_model import LogisticRegression
import mlflow
with mlflow.start_run(run_name="baseline_majority"):
mlflow.log_param("data_hash", DATA_HASH)
mlflow.log_param("kind", "majority")
m = DummyClassifier(strategy="most_frequent").fit(X_train, y_train)
log_metrics_with_ci(m, X_val, y_val)
with mlflow.start_run(run_name="baseline_logreg"):
mlflow.log_param("data_hash", DATA_HASH)
mlflow.log_param("kind", "logreg")
m = LogisticRegression(max_iter=1000).fit(X_train, y_train)
log_metrics_with_ci(m, X_val, y_val)Log: primary metric ± CI, segment metrics, calibration, prediction latency.
Anti-Patterns
| Anti-pattern | Cost |
|---|---|
| Skip to XGBoost "to save time" | No reference; cannot tell if features help |
| Baseline on different data than the model | Comparison is meaningless |
| Baseline once, never re-run | Data drifts; old baseline lies |
| Reporting "model A vs. model B" without baseline | Neither may beat trivial |
| Baseline only on global metric | Segment performance unknown |
Red Flags
- Strong baseline beats deploy threshold and you're still building a deep model "because it's interesting."
- Complex model beats baseline by less than the CI width.
- You can't show the baseline run in MLflow.
Verification Checklist
- [ ] Trivial baseline implemented and tracked.
- [ ] Strong baseline implemented and tracked.
- [ ] Both use the same data hash and validation strategy as planned complex models.
- [ ] Scores ± CI documented in spec §5.
- [ ] Decision recorded: ship baseline, or authorize complex modeling.
Cross-References
- REQUIRED BEFORE:
datapowers:test-driven-modeling - REQUIRED WITH:
datapowers:validation-strategy-design,datapowers:experiment-tracking,datapowers:model-evaluation-rigorously
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