A measure of how well a model class can fit random noise, indicating capacity and generalization ability.
A collection of data examples used for training, validating, or testing machine learning models.
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
When a model fits its training data too closely, noise included, so it scores well on examples it has seen and poorly on new ones.
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
A function that scores how wrong a model's prediction is as a single number, which training then works to make as small as possible.
The principle of choosing a model that minimizes error on training data, fundamental to supervised learning.
Probably Approximately Correct - a theoretical framework for analyzing learning algorithm guarantees.
A measure of how well a model class can fit random noise, indicating capacity and generalization ability.
This concept is essential for understanding machine learning fundamentals and forms a key part of modern AI systems.
Leads to
Nothing yet: a destination in its own right.