Probabilistic Classifiers and Calibration
This module helps learners use algorithms and checks where probabilities matter.
Browser lab plus download fallbackRead the lessons, open the module notebook in the browser lab when available, and keep the downloadable pack for a local copy. Write the algorithm decision evidence before chasing scores.
Notebook options
Use the browser lab for quick practice, or extract the ZIP and open modules/module-04/notebooks/module-04-algorithm-notebook.ipynb locally. The downloadable pack remains the fallback if browser storage, network, or device limits interrupt the lab.
Module sequence
- Scores are not automatically reliable probabilitiesUnit 1 · 25-40 minutes
- Gaussian, Multinomial, Bernoulli, and Categorical Naive BayesUnit 2 · 25-40 minutes
- Naive assumptions and why the model can still be usefulUnit 3 · 25-40 minutes
- Probability calibration curvesUnit 4 · 25-40 minutes
- Calibrated classifiersUnit 5 · 25-40 minutes
- Thresholds after calibrationUnit 6 · 25-40 minutes
- Project step: probability-quality reportUnit 7 · 25-40 minutes
- Activity: Create a probability-quality reportPractice activity · 45-75 minutes
- Module 4 Assessment: Probabilistic Classifiers and Calibration CheckModule check · 30-45 minutes