End-to-End Imbalanced Pipeline
Put the fixes together cleanly.
End-to-End Imbalanced Pipeline is a free NLP Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Put It All Together
Now you will chain every fix into one clean flow. A single pipeline keeps vectorizing, resampling, and modeling in order.
Split First, Always
Begin with a stratified split so train and test keep the same rare-class ratio. This protects honest evaluation.
X_tr, X_te, y_tr, y_te = train_test_split(X, y, stratify=y)Vectorize the Text
Turn raw text into numbers with TF-IDF so the classifier has features to learn from.
tfidf = TfidfVectorizer()Resample Inside the Pipeline
Use an imblearn Pipeline so SMOTE runs only on training folds, never leaking into validation or test.
from imblearn.pipeline import PipelineWire the Stages
List the stages in order: vectorizer, then SMOTE, then model. The pipeline runs them as one fitted object.
pipe = Pipeline([('tf', tfidf), ('sm', SMOTE()), ('clf', LogisticRegression())])Fit Once, Cleanly
Call fit on the whole pipeline. Every step trains in sequence using only your training data.
pipe.fit(X_tr, y_tr)Evaluate the Right Way
Skip raw accuracy. A classification report shows precision, recall, and F1 for the rare class clearly.
from sklearn.metrics import classification_report
print(classification_report(y_te, pipe.predict(X_te)))Validate Without Leaks
Pair the pipeline with stratified cross-validation so resampling happens fresh inside each fold.
Tune the Threshold
Get probabilities from the pipeline and pick a threshold that hits your target recall on validation data.
proba = pipe.predict_proba(X_te)[:, 1]Save the Whole Pipeline
Persist the entire fitted pipeline with joblib so inference repeats every step automatically later.
import joblib
joblib.dump(pipe, 'model.joblib')One Object, Reproducible
Because vectorizing, balancing, and modeling live together, your results stay reproducible and free of subtle leaks. 🚀
Quick Check
One more on safe pipelines.
Recap
Stratify, vectorize, resample inside a pipeline, judge by F1 and recall, tune the threshold, then save it all. 🚀
Frequently asked questions
Is the “End-to-End Imbalanced Pipeline” lesson free?
Yes — the full text of “End-to-End Imbalanced Pipeline” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “End-to-End Imbalanced Pipeline”?
Put the fixes together cleanly. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “End-to-End Imbalanced Pipeline” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Why Rare Classes Get Ignored
- Resampling and Class Weights
- Choosing Threshold and Metric
- End-to-End Imbalanced Pipeline