Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
In-depth explanation
Cross-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
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
Hyperparameter
Configuration settings set before training that control the learning process, not learned from data.
Master Cross-Validation.
Learn how to apply this concept with hands-on projects in our comprehensive AI programs.