Overview
The bias-variance tradeoff is a crucial concept in machine learning that helps practitioners understand the sources of error in their models. Bias refers to the error introduced by overly simplistic assumptions, while variance refers to the error due to excessive sensitivity to fluctuations in the t...
Key Terms
Example: A linear model predicting a non-linear relationship has high bias.
Example: A complex model that fits every data point perfectly has high variance.
Example: A model that performs well on training data but poorly on unseen data.
Example: A linear model trying to fit a quadratic relationship.
Example: Using k-fold cross-validation to evaluate model performance.
Example: Lasso and Ridge regression are common regularization methods.
Related Topics
Model Evaluation
Learn how to evaluate the performance of machine learning models using various metrics.
intermediateFeature Selection
Understand the importance of selecting the right features to improve model performance.
intermediateEnsemble Methods
Explore techniques that combine multiple models to improve predictions.
advancedKey Concepts
BiasVarianceOverfittingUnderfitting