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

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評価

難易度
1/5
見積もり時間
1時間未満
初心者へのやさしさ
35/100
issue の種類
ドキュメント
明瞭さ
明確に書かれている
活発さ
活発
技術スタック
docker, numpy, python, scikit-learn
領域
documentation, machine-learning

調査の方向性

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.

索引モデルが issue の本文から書いたものです。

説明

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
主要言語
Python
スター
7
フォーク
20
平均マージ
7日 9時間
マージ済み PR（30日）
8

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