19 · Kindle sentiment: production TF-IDF pipeline
Production-shape the Kindle sentiment classifier: TF-IDF plus GridSearchCV plus joblib persistence — the deliverable form of any text classifier.
Production-shape the Kindle sentiment classifier with TF-IDF plus GridSearchCV plus persistence — the deliverable form of any text classifier.
Without this:
You'd ship a Jupyter cell instead of a reusable model.
This lesson is the synthesis of the entire NLP track. You have tokenized text, explored BOW, TF-IDF, and Word2Vec, built spam classifiers, and compared feature representations. Now you will put all of that together into a production-shaped text classification pipeline.
"Production-shaped" means three things:
- Held-out test set: the test set is split off first and never touched during development or tuning. Its result is the honest reported accuracy.
- Hyperparameter tuning on TRAIN only: GridSearchCV cross-validates entirely within the training set. The test set has zero influence on parameter selection.
- Persistent artifact: the trained pipeline is serialized to disk with joblib, reloaded, and verified — the exact shape of a microservice that calls
pipe.predict_proba.
The task remains Kindle sentiment on the same 40 synthetic reviews. The goal is not to squeeze out the last 0.5 % accuracy on 40 rows — it is to demonstrate the engineering scaffolding that wraps any classical NLP model.
After this lesson you will also inspect model interpretability (which tokens most strongly signal positive vs negative sentiment) — because every deployed model must be explainable to its stakeholders.
Python (in browser)
Python runs entirely in your browser via Pyodide (~6 MB on first Run, cached after).
Python (in browser)
Python runs entirely in your browser via Pyodide (~6 MB on first Run, cached after).
Python (in browser)
Python runs entirely in your browser via Pyodide (~6 MB on first Run, cached after).
Python (in browser)
Python runs entirely in your browser via Pyodide (~6 MB on first Run, cached after).
Python (in browser)
Python runs entirely in your browser via Pyodide (~6 MB on first Run, cached after).
Python (in browser)
Python runs entirely in your browser via Pyodide (~6 MB on first Run, cached after).
Production checklist: full pipeline, test holdout, CV tuning on train only, joblib, seed, audit log
Why tune hyperparameters on the TRAIN CV folds, not the test set?
- Production text classifiers use a 3-way split: train (GridSearchCV CV folds), optional val (quick sanity checks), test (held out until final evaluation). The test set is never touched during tuning.
- sklearn Pipeline plus GridSearchCV is the canonical tuning pattern: define a pipeline, define a param_grid with double-underscore notation (tfidf__ngram_range, clf__C), run GridSearchCV on train data, read best_params_ and best_estimator_.
- Model interpretability via LogReg coefficients: coef_[0] maps each TF-IDF feature to its contribution to the positive class. Top positive coefs are positive-sentiment signals; top negative coefs are negative-sentiment signals. Always inspect before shipping.
- joblib.dump(pipe, path) plus joblib.load(path) is the deployment artifact pattern. The serialized pipeline includes both TfidfVectorizer (vocabulary and IDF weights) and the classifier. Load it in any Flask or FastAPI endpoint and call pipe.predict_proba on raw strings.
This is the final deliverable shape of any classical NLP project; the same pattern works for product categorisation, support-ticket routing, intent classification, and abuse detection.
If you remove it: Without it, you'd ship Jupyter cells instead of reusable models.