Feature Engineering vocabulary helps readers understand feature, target, encoding, scaling, normalization and the other concepts behind reliable artificial intelligence. Learning the language of feature engineering makes technical tutorials, model evaluations and practical decisions easier to understand. Instead of memorising disconnected jargon, start by asking what each term describes, what evidence supports it and why it matters.
The core aim of vocabulary mastery for feature engineering is to turn raw observations into useful model inputs while preventing information leakage and preserving valid evaluation. A good learner can define a term accurately, compare it with a neighbouring concept, use it in a realistic example and recognise when an apparently confident conclusion goes beyond the evidence.
This article belongs to the eduKateSG Vocabulary Hub and connects to Machine Learning Vocabulary.
The core learning map
- Identify: Name the object, measurement or decision involved.
- Distinguish: Separate nearby terms that answer different questions.
- Apply: Use the language in a real scenario.
- Verify: Check evidence, assumptions and limitations.
- Communicate: Explain the conclusion in clear everyday English.
Ten essential feature engineering terms
- feature — identify where this concept appears in the workflow; then practise using the term accurately in a sentence.
- target — explain what it represents or measures; then practise using the term accurately in a sentence.
- encoding — connect it to a real example; then practise using the term accurately in a sentence.
- scaling — compare it with a related concept; then practise using the term accurately in a sentence.
- normalization — describe its practical consequence; then practise using the term accurately in a sentence.
- imputation — identify where this concept appears in the workflow; then practise using the term accurately in a sentence.
- interaction feature — explain what it represents or measures; then practise using the term accurately in a sentence.
- categorical variable — connect it to a real example; then practise using the term accurately in a sentence.
- data leakage — compare it with a related concept; then practise using the term accurately in a sentence.
- feature selection — describe its practical consequence; then practise using the term accurately in a sentence.
A worked real-world example
A tutor wants to predict which revision topics may need attention. Raw scores, assessment dates and topic labels become candidate features, but a score from an assessment taken after the prediction date must never be included.
Three distinctions that matter
Feature vs target
A feature is information available for prediction; the target is the outcome being predicted.
Encoding vs scaling
Encoding represents categories numerically; scaling changes numerical magnitude or distribution.
Feature selection vs feature engineering
Selection chooses existing inputs; engineering creates or transforms inputs.
How to practise
Construct a five-column sample dataset and identify which columns are available at prediction time. Propose two derived features, then test whether they could leak future information.
- Write one sentence defining the main concept.
- Draw a simple process diagram and label its stages.
- Explain the worked example without jargon.
- Identify a limitation or potential source of error.
- Teach the idea to another learner and invite a question.
- Return to the topic after a week using a new example.
Common mistakes to avoid
- Definition without application: knowing a word but not recognising it in a problem.
- Metric without context: reporting a number without its population, baseline or assumptions.
- Correlation without causation: interpreting association as proof of cause.
- One-size-fits-all advice: ignoring the task, dataset and consequences of errors.
Frequently asked questions
What is feature engineering vocabulary?
It is the specialised language used to describe feature, target, encoding, scaling, normalization, imputation and related decisions.
Which words should beginners learn first?
Begin with feature, target, encoding, scaling. Learn each in a worked context before adding advanced terminology.
How do I know I understand a term?
You can explain it in your own words, distinguish it from a similar term and use it correctly in a new scenario.
What is the best way to remember the vocabulary?
Use retrieval practice, spaced review and explanation through examples rather than copying definitions repeatedly.
Related vocabulary learning
The Feature Engineering vocabulary standard
Mastery means the learner can turn raw observations into useful model inputs while preventing information leakage and preserving valid evaluation, and communicate the result clearly enough for someone else to examine the reasoning.