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[FAQ] HW5 (2026): predict_proba differs — locked env, artifact checksum, normalize_record

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Evaluación

Dificultad
1/5
Tiempo estimado
Menos de una hora
Aptitud para principiantes
35/100
Tipo de issue
Documentación
Claridad
Bien especificado
Estado de actividad
Activo
Stack tecnológico
docker, numpy, python, scikit-learn
Área
documentation, machine-learning

Línea de trabajo

The issue already provides a full FAQ answer, including commands and references to cohorts/2026/homework/05-deployment/homework.md and model.py. Check whether that guidance matches the frozen homework release and API behavior; done means the answer is accurate and resolves the reported probability mismatch. The FAQ checklist says the answer is already complete, so this is not an evident first contribution.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Course

machine-learning-zoomcamp

Question

Homework 5 (2026): Why do my predict_proba results differ from the expected options, even though my code runs without errors?

Answer

Fix first — make sure you run the frozen 2026 release exactly as checked in, without retraining or regenerating anything:

  1. Verify the artifact before using it (from cohorts/2026/homework/05-deployment/):

    sha256sum pipeline.bin

    It must match the hash in homework.md. If it differs, re-download the directory instead of debugging numbers.

  2. Use the locked environment, don't regenerate it:

    uv sync --locked

    Do not run uv sync without --locked and do not regenerate uv.lock. A different scikit-learn/numpy build can shift probabilities.

  3. Normalize the lead exactly like the API does (model.py):

    import pickle
from model import normalize_record

with open("pipeline.bin", "rb") as f:
    pipeline = pickle.load(f)

record = normalize_record(lead)  # training-set medians, "NA" for missing categoricals
prob = float(pipeline.predict_proba([record])[0, 1])

    Hand-rolling imputation (e.g. fillna(0)) instead of normalize_record changes the vector and the probability.

  4. Serve and containerize without edits:

    uv run uvicorn predict:app --host 0.0.0.0 --port 9696
docker build -t zoomcamp-model:2026-hw5 .
docker run --rm -p 9696:9696 zoomcamp-model:2026-hw5
curl -s http://localhost:9696/health

    Free port 9696 first (stop any local uvicorn). The container must return the same inference as local; if it doesn't, the artifact or lockfile inside the build context differs.

Reference: cohorts/2026/homework/05-deployment/homework.md (frozen lead-scoring release, DictVectorizer + LogisticRegression(solver="liblinear", C=1.0)).

Checklist

  • I have searched existing FAQs and this question is not already answered
  • The answer provides accurate, helpful information
  • I have included any relevant code examples or links
Lenguaje dominante
Python
Estrellas
7
Forks
20
Merge medio
7 d 9 h
PR fusionados (30 d)
8

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  4. Abre un pull request que haga referencia al número del issue.

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