💡 Target Variable

Target Variable#

The quantity a supervised model is trained to predict.

Important

✨ AI-generated content. This page was written with the assistance of an AI language model and is provided as a learning aid. Despite careful review, it may still contain mistakes, omissions, or out-of-date information. Whether you are new to the topic, a team lead, or a senior practitioner, treat it as a starting point rather than an authoritative reference: read it critically and independently verify anything you act on (code, commands, figures, and factual claims) against official documentation and primary sources before relying on it.

What it is#

The target variable (\(y\)) — also called the dependent, response, or outcome variable, or the label — is the quantity a supervised model is trained to predict from the input features (the independent variables). It is the “correct answer” that must be observed in the training data.

Types#

It can be continuous (a regression target, e.g. a price) or categorical (a classification target, e.g. spam / not-spam), and also ordinal or multi-label. Its type determines the problem and which models fit.

Why it matters#

The algorithm only ever learns a function mapping features to target, so a well-defined target is decisive: without a labeled target, supervised learning cannot proceed, and a poorly chosen or biased target propagates straight into the model’s behavior.

Theme: AI & ML Concepts · All terminology

Hint

Mind map — connected ideas

Feature Values · Classification Probability · Regression Coefficient · Point Forecasts · Probabilistic Forecasts · Label Noise

Hint

More in AI & ML Concepts

AI (Artificial Intelligence) · Classification Models · Computer Vision (CV) · Decision Trees · Linear Models · LLMs (Large Language Models) · Logistic Regression · Machine Learning (ML) · Medical AI · Natural Language Processing (NLP) · Neural Networks · Regression Models · Support Vector Machines (SVMs)