Overview
Model generalization is a fundamental concept in machine learning that determines how well a model can perform on unseen data. It is essential for creating models that are not only accurate on training data but also reliable in real-world applications. Understanding the balance between overfitting a...
Key Terms
Example: A model that predicts training data perfectly but fails on new data.
Example: A linear model trying to fit a complex, non-linear dataset.
Example: Images of cats and dogs used to train a classifier.
Example: A set of images not seen by the model during training to check its accuracy.
Example: L1 and L2 regularization methods.
Example: K-fold cross-validation where the dataset is divided into K subsets.
Related Topics
Bias-Variance Tradeoff
Understanding the balance between bias and variance is crucial for model performance.
intermediateFeature Engineering
The process of selecting and transforming variables to improve model performance.
intermediateEnsemble Learning
Combining multiple models to improve overall performance and generalization.
advancedKey Concepts
OverfittingUnderfittingTraining DataValidation Data