Overview
The bias-variance trade-off is a crucial concept in machine learning that helps in understanding the errors that can occur in predictive models. Bias refers to the error due to overly simplistic assumptions, while variance refers to the error due to excessive complexity. Striking a balance between t...
Key Terms
Example: A linear model predicting a non-linear relationship has high bias.
Example: A complex model that fits noise in the training data 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: A polynomial regression model of degree 10 is more complex than one of degree 2.