Overview
Overfitting and generalization are critical concepts in machine learning that determine how well a model performs on new data. Overfitting occurs when a model learns the training data too well, including noise, which leads to poor performance on unseen data. Generalization, on the other hand, is the...
Key Terms
Example: A model that predicts training data perfectly but fails on new data.
Example: A model that accurately predicts outcomes for new data points.
Example: A collection of images used to teach a model to recognize objects.
Example: Data set aside to tune model parameters.
Example: L1 and L2 regularization methods.
Example: K-fold cross-validation.
Related Topics
Bias-Variance Tradeoff
Understanding the balance between bias and variance is crucial for model performance.
intermediateModel Evaluation Metrics
Learn about different metrics used to evaluate model performance.
intermediateFeature Selection Techniques
Explore methods for selecting the most relevant features for your model.
intermediateKey Concepts
OverfittingGeneralizationTraining DataValidation Data