Machine Learning Foundations

Training Data, explained

Training data is the collection of examples used to fit a machine learning model.

Part of the Semantic Notion technical glossary.

Short Definition

Training data is the collection of examples used to fit a machine learning model.

Intuition

The model can only learn from the examples it sees. If the training data is biased, incomplete, or noisy, the learned behavior usually reflects those limits.

Technical Definition

Training data is the sample used by an optimization procedure to estimate model parameters, usually distinct from validation and test data.

Example

Images of cats and dogs with labels can train an image classifier.

Common Misunderstandings

More training data is not always better if the additional data is low quality.

Training performance alone does not prove generalization.

Same Category

More from Machine Learning Foundations

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.

Essays that use this concept

Data Science and EvaluationOverfitting vs Underfitting vs Generalization

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