How to learn Baseline Modeling Discipline for Feature Engineering For Classical NLP in NLP Engineer for free

Who this is for

NLP Engineers and ML practitioners who build classical NLP systems (TF-IDF, bag-of-words, linear models) and want a reliable, repeatable way to set and beat baselines before adding complexity.

Prerequisites

Comfortable with Python or another language for ML experiments.
Basic understanding of metrics: precision, recall, F1, ROC-AUC/PR-AUC.
Knowledge of train/validation/test splits and cross-validation.
Familiarity with TF-IDF and linear models (Logistic Regression or Linear SVM).

Why this matters

Baseline modeling discipline helps you ship reliable NLP solutions faster and safer. Real tasks you will face:

Quickly verifying if a business problem is solvable with simple features before investing in complex models.
Setting acceptance criteria for stakeholders (e.g., "+5 macro-F1 over TF-IDF + Logistic Regression").
Preventing data leakage by following a clean, repeatable workflow.
Explaining performance trade-offs using transparent baselines and error breakdowns.
Creating a safe fallback model that is easy to maintain and debug.

Concept explained simply

Baseline modeling discipline is a habit: start with simple, transparent models, prove a reliable yardstick, and require every new idea to beat it fairly. Think of it as defensive driving for ML experiments.

Mental model: guardrails and yardsticks

Guardrails: rules that prevent bad experiments (leakage, cherry-picking, unreproducible results).
Yardsticks: simple, trustworthy baselines that any new model must beat with a margin.

Core rules

Define the task and metric clearly (e.g., macro-F1 for imbalanced classification).
Start with naive baselines: random, majority class, simple keyword/regex or lexicon heuristic.
Add a simple ML baseline: TF-IDF + linear model with reproducible settings.
Use proper splits: stratified train/validation/test; never peek at test during development.
Reproducibility: fix random seeds, record dataset version and preprocessing choices.
Evaluate fairly: report multiple metrics, confidence intervals if possible, and per-class scores.
Sanity checks: shuffled-label test should drop to chance; tiny-slice tests should behave intuitively.
Freeze the baseline: log config and metrics; require a clear margin and a significance check to accept new models.

Baseline types — quick reference

Trivial: random guess, stratified random, majority class.
Heuristic: keyword/regex rules, sentiment lexicon counts, most-frequent-tag per token (for sequence labeling).
Simple ML: TF-IDF + Logistic Regression/Linear SVM (often surprisingly strong).
Upper bound (ceiling): human performance on a sample or an oracle feature ablation to estimate headroom.

Sanity-check recipes

Shuffle labels: performance should drop to chance (e.g., ~0.5 macro-F1 in balanced binary tasks).
Vocabulary leak check: build vectorizers only on training folds; never on the full dataset before splitting.
Class imbalance check: compare macro-F1 vs accuracy; big gaps signal imbalance.
Slice tests: evaluate on short texts, long texts, and noisy texts separately.

Set up your baseline workflow

Write the task statement and metric. Example: "Binary sentiment; primary metric = macro-F1; secondary = accuracy."
Freeze data handling. Stratified split into train/validation/test. Keep test untouched until the end.
Implement naive baselines (majority, random, simple keyword). Record metrics.
Implement TF-IDF + linear model. Use cross-validation on train for a stable estimate; then evaluate once on test.
Run sanity checks (shuffle labels, slice tests). If anything fails, fix before proceeding.
Log everything: seed, split method, vectorizer settings, model hyperparameters, metrics, confusion matrix.
Freeze as "Baseline v1". Define a margin for acceptance (e.g., +2–3 macro-F1) and a significance test plan.

Pro tips for reliable baselines

Use stratified K-fold cross-validation (e.g., 5 folds) for stable estimates on small datasets.
Prefer macro-F1 or PR-AUC when classes are imbalanced.
For text classification, try unigrams+bigrams TF-IDF and a linear model before anything else.
Document all preprocessing (lowercasing, stopwords, min_df). Small changes can shift metrics.

Worked examples

Example 1: Sentiment classification (balanced)

Naive baselines: majority accuracy ≈ 0.50; macro-F1 ≈ 0.50.
TF-IDF + Logistic Regression: CV macro-F1 ≈ 0.78–0.90; test macro-F1 often ≥ 0.75 on clean data.
Sanity: shuffled-label macro-F1 ≈ chance (≈ 0.5); if not, investigate leakage.

Example 2: Spam detection (imbalanced)

Naive majority: high accuracy (e.g., 0.90) but poor recall for spam — misleading.
Metric choice: PR-AUC and macro-F1 to value minority class. Consider class_weight="balanced".
TF-IDF + Linear SVM: improves minority recall; tune threshold on validation to balance precision/recall.

Example 3: NER (sequence labeling)

Naive baseline: tag each token with its most frequent tag seen in training; unknown tokens → "O".
Heuristic baseline: simple dictionary for names + capitalization features.
Simple ML baseline: CRF with word shape + affix + cluster features; evaluate with entity-level F1.

Exercises

Complete the two exercises below. Use the checklist to confirm your baseline discipline before you submit.

Exercise 1: Build majority and TF-IDF + Logistic Regression baselines on a toy sentiment set. Report macro-F1, accuracy, confusion matrix, and sanity checks.
Exercise 2: Design a baseline plan for an imbalanced intent classification task. Choose metrics, splits, baselines, success criteria, and a reporting template.

Submission checklist

Clear task statement and chosen primary/secondary metrics.
Stratified split with fixed random seed recorded.
Naive baselines implemented and logged.
TF-IDF + linear baseline with CV estimate and a single test evaluation.
Sanity checks run (shuffle-label and at least one slice test).
All hyperparameters and metrics recorded; short error analysis included.

Common mistakes and how to self-check

Leakage via vectorizer: Did you fit TF-IDF on the whole dataset before splitting? If yes, redo with train-only.
Overfitting to test: Did you peek at test scores while tuning? If yes, create a new final test or reset tuning.
Wrong metric for imbalance: Are accuracy and macro-F1 diverging a lot? Use macro-F1/PR-AUC and per-class scores.
No seed control: Can you reproduce results exactly? If not, fix seeds and log versions.
Unstable estimates: Are results jumping across runs? Use cross-validation and report mean±std.

Practical projects

Product review sentiment: establish majority/keyword baselines, then TF-IDF + Logistic Regression; ship a baseline API.
Support email triage: imbalanced multi-class; choose macro-F1 + PR-AUC, add class weights, and slice by sender domain.
Rule vs model showdown: build a regex/keyword heuristic and compare fairly to a TF-IDF baseline on a frozen test set.

Learning path

After baselines: feature scaling of TF-IDF outputs, n-gram exploration, character n-grams for noisy text.
Model calibration: reliability curves and threshold selection.
Error analysis: confusion hotspots, per-class failures, and iterative ablation.
Transition to neural baselines only after you beat the classical baseline with a fair protocol.

Mini challenge

Pick any small dataset you have (or use the toy one from Exercise 1). Create a baseline scoreboard (majority, heuristic, TF-IDF + linear). Define a +3 macro-F1 acceptance margin. Propose one feature change (e.g., add character n-grams) and test if it beats the margin with a significance check plan.

Hints

Keep the test set untouched; compare changes via cross-validation on train or with a fixed validation set.
Use stratified bootstrap of paired predictions to estimate confidence in the improvement.

Quick Test

The quick test is available to everyone. Only logged-in users will have their progress saved.

Next steps

Run the Quick Test below to check your understanding.
Apply the discipline to your next real task and freeze a Baseline v1.
Move on to deeper feature engineering once your baseline is solid and reproducible.

Instructions

Use the tiny dataset below to establish baselines. Keep everything reproducible.

Dataset (texts, labels):

texts = [
  "I love this movie, it was fantastic!",
  "Absolutely terrible and boring.",
  "Great acting and plot.",
  "Worst film ever.",
  "I enjoyed every minute.",
  "Not good, not bad, just ok.",
  "Horrible experience.",
  "What a masterpiece!",
  "Mediocre at best.",
  "I would watch it again.",
  "Painfully dull.",
  "Loved the soundtrack and visuals."
]
labels = [
  "pos","neg","pos","neg","pos","neg",
  "neg","pos","neg","pos","neg","pos"
]

Split the data into train/test with stratification, test_size=0.2, random_state=42.
Naive baseline: majority-class classifier. Report accuracy and macro-F1 on test.
Simple ML baseline: TF-IDF (unigrams+bigrams) + Logistic Regression (max_iter=1000). Use 5-fold CV on train to estimate macro-F1. Then fit on train and evaluate once on test: accuracy, macro-F1, per-class precision/recall/F1, and confusion matrix.
Sanity check: shuffle the training labels, retrain, and confirm performance drops to chance.
Log: seed, vectorizer settings, model hyperparameters, and metrics.

Note: Small datasets lead to noisy test metrics; that is why you also report cross-validation mean±std.

Baseline Modeling Discipline

Table of Contents