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
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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