Standard 2 stops to get here · leads to 1
Underfitting
When a model is too simple to capture the underlying pattern in data, performing poorly on both training and test sets.
Your route here
2 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Training Data ✓ understood
The examples a model learns its weights from, kept separate from the validation and test data used to check how well it generalizes.
- Underfitting · you are here ✓ understood
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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. Foundations Bias-Variance Tradeoff The balance between a model's bias (systematic error) and variance (sensitivity to training data fluctuations). Training Regularization Techniques to prevent overfitting by adding constraints or penalties to the model (L1, L2, dropout, early stopping). Training Hyperparameter Tuning The process of finding optimal hyperparameter values through techniques like grid search, random search, or Bayesian optimization.