Predictive Analytics for Text
Model Understanding and Generative Alignment Laboratory (MUGA LAB)
Academic Year 2025–2026
Course Overview
Predictive Analytics for Text introduces supervised learning techniques for text-based prediction,
emphasizing reproducibility, interpretability, and uncertainty estimation.
Students learn to:
- Preprocess and vectorize natural language text
- Build and evaluate classification and regression models
- Train deep architectures (MLP, RNN, Transformer, TabNet)
- Integrate probabilistic inference using BayesFlow
- Communicate findings through interpretable technical reports
Course Objectives
By the end of the course, students should be able to:
| Objective | Description |
|---|---|
| Text Preprocessing | Tokenize, lemmatize, and normalize text using spaCy/NLTK |
| Vectorization | Implement BoW, TF–IDF, and embedding-based representations |
| Supervised Learning | Train and evaluate classification and regression models |
| Feature Analysis | Use SHAP and calibration metrics to interpret model behavior |
| Deep Learning | Construct MLPs, RNNs, and Transformer-based architectures |
| Uncertainty Modeling | Estimate predictive uncertainty using BayesFlow |
| Model Integration | Combine deterministic and probabilistic models into ensembles |
| Scientific Communication | Write structured, interpretable reports and presentations |
Weekly Schedule
| Week | Topics | Key Models / Methods |
|---|---|---|
| 1 | Text preprocessing — tokenization, lemmatization, POS, NER | spaCy, NLTK |
| 2 | Vectorization and embeddings | TF–IDF, Word2Vec, GloVe, FastText |
| 3 | Classification I | Perceptron, Adaline, Logistic Regression, Naive Bayes |
| 4 | Classification II | SVM, Random Forest, XGBoost |
| 5 | Regression models | Linear, SVR, Random Forest, XGBoost |
| 6 | Evaluation and validation | Accuracy, F1, ROC AUC, MSE, MAE, R² |
| 7 | Feature analysis and tuning | Feature importance, SHAP, Optuna |
| 8 | Deep Learning I | MLP, TF–IDF or embedding inputs |
| 9 | Deep Learning II | GRU, LSTM sequence modeling |
| 10 | Transformers + TabNet | Attention, positional encoding, mask interpretation |
| 11 | BayesFlow for Uncertainty | Posterior inference, calibration |
| 12 | Ensemble Learning | Stacking classical + deep + probabilistic models |
| 13–15 | Capstone Project | Implementation, checkpoints, final defense |
Tools and Frameworks
| Domain | Libraries / Tools |
|---|---|
| Text Processing | spaCy, NLTK, pandas, NumPy |
| ML & Evaluation | scikit-learn, XGBoost, Optuna |
| Deep Learning | PyTorch, torchtext, transformers, pytorch-tabnet |
| Uncertainty & Interpretability | BayesFlow, SHAP, matplotlib, seaborn |
Assessment Breakdown
| Component | Weight |
|---|---|
| Chapter Critiques (6 total) | 30% |
| Long Tests (2) | 30% |
| Capstone Project | 30% |
| Assignments | 10% |
Capstone Project
Each student (or team) develops an end-to-end predictive pipeline for text data.
Requirements:
- Must include at least one deep model and BayesFlow integration
- Ensemble with interpretability (e.g., SHAP + calibration analysis)
- Submission: 5–7 page technical report + oral presentation
Evaluation Criteria:
- Predictive performance and interpretability
- Reproducibility (fixed splits, random seeds, documented setup)
- Clarity and depth of reflection in the report
Alignment with MUGA LAB Framework
This course operates within the MUGA LAB Pedagogical Stack, integrating:
| Layer | Description |
|---|---|
| Mathematical Foundation | Linear models, optimization, calibration theory |
| Computational Layer | Reproducible ML pipelines (TF–IDF, embeddings, SHAP, DEHB) |
| Pedagogical Layer | Structured prompt engineering and interpretive reflection |
| Research Integration | Links to Model Understanding and Generative Alignment (2025) |
Related Notebooks
| Module | Description | Folder |
|---|---|---|
| Vectorization — Traditional | Bag-of-Words and TF–IDF workflows | /courses/predictive_analytics_for_text/notebooks/W02_Traditional_Vectorization_v1.1.ipynb |
| Vectorization — Embeddings | Word2Vec, FastText, GloVe, BayesFlow integration | /courses/predictive_analytics_for_text/notebooks/W02_Embedding_Vectorization_v1.1.ipynb |
| Master Split Generation | Reproducible train/val/test index generation | /courses/predictive_analytics_for_text/scripts/generate_master_split.py |
| Indexing & Preprocessing Helpers | Text and split utilities | /helpers/ |
Contact
MUGA LAB — mugalab.research@gmail.com
https://lexmuga.github.io/mugalab