Machine Learning Foundations

Model, explained

A model is the learned function or system that maps inputs to outputs after training.
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Part of the Semantic Notion technical glossary.

Short Definition

A model is the learned function or system that maps inputs to outputs after training.

Intuition

The model is the thing you use after learning is finished. It carries the pattern extracted from training data.

Technical Definition

A model is a parameterized mathematical function, probabilistic system, or learned program whose parameters are estimated from data.

Example

A trained fraud detector scores new transactions for risk.

Common Misunderstandings

A model is not the same as the dataset used to train it.
A larger model is not automatically a better model for every task.

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.

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