data/artifacts/models/bloom_classifier/model_card.md

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.