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Imbalanced Dataset
A dataset where classes have significantly different numbers of examples, causing models to bias toward majority classes.
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4 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Classification ✓ understood
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
- Imbalanced Dataset · you are here ✓ understood
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Training Focal Loss A modified cross-entropy loss that down-weights easy examples, helping with class imbalance. Evaluation F1 Score The harmonic mean of precision and recall, providing a single metric that balances both concerns. Evaluation Precision The proportion of true positives among all positive predictions - measures how many predicted positives are actually positive. Evaluation Recall The proportion of true positives among all actual positives - measures how many actual positives were correctly identified. Training Data Augmentation Creating variations of training data through transformations (rotation, cropping, noise) to improve model generalization.