Term
Cross-Validation (ˈkrɒs-ˌvæ-lɪ-ˈdeɪ-shən)
Definition
Cross-validation is a method used in machine learning to check how well a model performs on unseen data by using different subsets of the data for training and testing.
Where you'll find it
Cross-validation is commonly used in machine learning platforms like TensorFlow, Scikit-learn, and PyTorch. You can find this feature in the model evaluation or testing sections, often within specific API methods dedicated to validation processes.
Common use cases
- Improving model reliability: Repeatedly training and testing the model on different data segments helps developers reduce the risk of overfitting.
- Selecting the model: It aids in comparing different models to see which performs best across various data subsets.
- Parameter tuning: This assists in fine-tuning model parameters to improve performance based on multiple test set results.
Things to watch out for
- Selection of method: Different cross-validation methods, such as k-fold or stratified k-fold, may be more suitable depending on data characteristics.
- Time consumption: More thorough cross-validation methods can significantly increase the time required to train and validate models.
- Complexity for beginners: Newcomers to machine learning may find it challenging to understand and implement effectively without guidance.
Related terms
- Overfitting
- Model Evaluation
- Training Dataset
- Test Dataset
- k-Fold Cross-Validation