Glossary Library

Technical GlossaryMachine Learning

Voting Ensemble

A simple but effective ensemble method that combines predictions from multiple models through majority vote or averaging.

A voting ensemble aims to produce more stable results by combining decisions from different models. In classification, majority voting is typically used, while in regression, predictions are averaged. This approach works particularly well when the models make different types of errors. Despite its simplicity, it is often a strong baseline ensemble strategy.

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Bernoulli Distribution

A discrete distribution that models single-step random experiments with only two possible outcomes.

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Mean Squared Error (MSE)

A common regression loss function that averages the squared differences between predictions and true values.

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Mean Absolute Error (MAE)

A regression loss function that averages the absolute differences between predictions and true values, offering greater robustness.