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GlossaryMachine LearningCross-Validation

Cross-Validation

[krɔs ˌvælɪˈdeɪʃən]
Machine Learning
Last updated: December 9, 2024

Definition

A technique for assessing how models perform on unseen data.

Detailed Explanation

Cross-Validation involves partitioning data into subsets training the model on some subsets and validating it on others. This method helps in evaluating model performance and selecting the best model parameters.

Use Cases

Model evaluation hyperparameter tuning preventing overfitting comparing different models.

Related Terms

Layer Normalization

A technique that normalizes the inputs across the features in a layer to stabilize and accelerate neural network training. Unlike batch normalization it operates on a single training example.

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Expectation-Maximization

An iterative algorithm for finding maximum likelihood estimates in models with latent variables.

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Recall

The proportion of actual positive cases that were correctly identified. Measures a model's ability to find all relevant cases within a dataset.

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