# Lecture 16 – Bias and Variance

Presented by Isaac Schmidt

Content by Isaac Schmidt, Andrew Bray, Suraj Rampure, Ani Adhikari

**Important:** You may want to review Lecture 3 for a refresher on random variables.

Video | Quick Check | |
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16.0 Introduction. |
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16.1 The data generation process, and the assumptions we make when choosing a model. |
16.1 | |

16.2 Continuous random variables. Expectation of a function of a random variable. Bias, revisited. |
16.2 | |

16.3 Variance of a random variable. Linear transformations of variance. Covariance and correlation. |
16.3 | |

16.4 Bias and variance in modeling. Simulation of different models with different samples. Components of prediction error. |
16.4 | |

16.5 Risk. Decomposition of model risk into the components of prediction error. |
16.5 | |

16.6 The Bias-Variance decomposition. Reasons and remedies for the different components of error. |
16.6 | |

16.7 Overfitting and underfitting. Complexity, and the bias-variance tradeoff. |
16.7 |