Machine Learning Foundations

Label, explained

A label is the target answer a supervised model is trained to predict.
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Part of the Semantic Notion technical glossary.

Short Definition

A label is the target answer a supervised model is trained to predict.

Intuition

Labels are the answer key. They tell the model what output should be associated with each training example.

Technical Definition

A label is the dependent variable or target value used in supervised learning objectives, classification losses, or regression losses.

Example

In a medical image dataset, the label may say whether a scan shows pneumonia.

Common Misunderstandings

Labels can be noisy or subjective.
A label is not necessarily the same as truth; it is the target recorded in the dataset.

Same Category

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BackpropagationBackpropagation is the algorithm used to compute gradients through a neural network efficiently.Bias-Variance TradeoffThe bias-variance tradeoff describes the balance between models that are too simple to capture structure and models that are too sensitive to training data noise.Binary ClassificationBinary classification is a prediction task where each example belongs to one of two classes.DropoutDropout is a regularization technique that randomly disables parts of a neural network during training.FeatureA feature is an input signal used by a model to make a prediction or learn a representation.Feature EngineeringFeature engineering is the process of transforming raw data into model inputs that make useful patterns easier to learn.

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