# Model Card: Bloom Classifier

## Model Details

- **Model Name:** bloom_classifier
- **Model Version:** bloom_classifier_v2_baseline_001
- **Algorithm:** TF-IDF + LogisticRegression (multinomial)
- **Framework:** scikit-learn
- **Trained At:** 2026-05-21T05:59:09.626503+00:00
- **Seed:** 42

## Intended Use

Automatically classify questions by Bloom's taxonomy cognitive level.
Used in the Bloom classification endpoint to predict one of 6 levels:
Remember, Understand, Apply, Analyze, Evaluate, Create.

## Training Data

- **Source:** training_bloom_classification.csv (synthetic dataset v2)
- **Split Counts:** train=3912, validation=1033, test=875
- **Feature:** question_text (TF-IDF vectorized, max_features=8000, ngram_range=(1,2))
- **Target:** bloom_level (6 classes)

## Metrics

### Validation Set
- Macro F1: 0.6434
- Weighted F1: 0.6634

### Test Set
- Macro F1: 0.6178
- Weighted F1: 0.6654

## Known Limitations

- Trained on synthetic data only — performance on real classroom questions is unknown.
- Class imbalance: Create (~2%) and Evaluate (~4%) are rare; recall on these classes may be low.
- TF-IDF features do not capture semantic similarity beyond n-gram overlap.
- Macro F1 is the primary metric; accuracy alone would mask poor performance on rare classes.

## Fallback Behavior

When the model is not loaded or confidence is below the threshold (0.55),
the system falls back to keyword heuristic classification:
define/list → Remember; explain → Understand; calculate/use → Apply;
compare/contrast → Analyze; justify → Evaluate; design → Create.