ml-rigor

# Machine Learning Rigor Patterns

## When to Use

Load this skill when building machine learning models. Every ML pipeline must demonstrate:
- **Baseline comparison**: Beat a dummy model before claiming success
- **Cross-validation**: Report variance, not just a single score
- **Interpretation**: Explain what the model learned
- **Leakage prevention**: Ensure no future information leaks into training

**Quality Gate**: ML findings without baseline comparison or cross-validation are marked as "Exploratory" in reports.

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## 1. Baseline Requirements

**Every model must be compared to baselines.** A model that can't beat a dummy classifier isn't learning anything useful.

### Always Compare To:
1. **DummyClassifier/DummyRegressor** - The absolute minimum bar
2. **Simple linear model** - LogisticRegression or LinearRegression
3. **Domain heuristic** (if available) - Rule-based approach

### Baseline Code Template

```python
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.model_selection import cross_val_score
import numpy as np

print("[DECISION] Establishing baselines before training complex models")

# Classification baselines
dummy_clf = DummyClassifier(strategy='most_frequent')
dummy_scores = cross_val_score(dummy_clf, X_train, y_train, cv=5, scoring='accuracy')
print(f"[METRIC:baseline_accuracy] {dummy_scores.mean():.3f} (majority class)")
print(f"[METRIC:baseline_accuracy_std] {dummy_scores.std():.3f}")

# Simple linear baseline
lr = LogisticRegression(max_iter=1000, random_state=42)
lr_scores = cross_val_score(lr, X_train, y_train, cv=5, scoring='accuracy')
print(f"[METRIC:linear_baseline_accuracy] {lr_scores.mean():.3f}")
print(f"[METRIC:linear_baseline_accuracy_std] {lr_scores.std():.3f}")

# For regression tasks
dummy_reg = DummyRegressor(strategy='mean')
dummy_rmse = cross_val_score(dummy_reg, X_train, y_train, cv=5,
                              scoring='neg_root