Standard 2 stops to get here
Cross-Validation
A technique for assessing model performance by partitioning data into subsets, training on some and validating on others.
Your route here
2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Train-Test Split ✓ understood
Dividing a dataset into separate portions for training the model and evaluating its performance on unseen data.
- Cross-Validation · you are here ✓ understood
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Evaluation Validation Set A portion of data held out from training, used to tune hyperparameters and monitor overfitting. Evaluation Test Set A final portion of data unseen during training and validation, used for unbiased evaluation of model performance. Training Overfitting 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. Training Hyperparameter Tuning The process of finding optimal hyperparameter values through techniques like grid search, random search, or Bayesian optimization. Evaluation Data Leakage When information from outside the training data is used to create the model, leading to overly optimistic performance estimates.