AI GlossaryCross-ValidationENTRY — Machine Learning
[Machine Learning]
Cross-Validation.
A technique to evaluate model performance by training and testing on different subsets of data.
In-depth explanation
01Cross-validation splits data into multiple folds, trains on some folds and tests on others, then averages results. K-fold cross-validation divides data into k parts, using each as a test set once. This provides a more reliable estimate of model performance than a single train-test split, especially with limited data.
Examples
02EX. 01
5-fold cross-validation
EX. 02
Leave-one-out cross-validation
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Ensemble LearningCombining multiple models to produce better predictions than any single model.03FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.04Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.05Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.06HyperparameterConfiguration settings set before training that control the learning process, not learned from data.
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