Learn Before
A Model Can Show Both Bias and Variance
A classifier with an estimated bias of 14% and variance of 16% has both kinds of error. It does poorly on the training set and becomes even less reliable on the dev set. In this situation, the usual labels of underfitting and overfitting are both partly true, so they do not describe the model very cleanly.
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Bias (Informal Definition)
Variance, Informally
Some Data Should Be Left Out of Training
Mean Squared Error and the Bias-Variance Decomposition
Why Estimate the Lowest Plausible Error?
When Increasing Capacity Helps One Error and Hurts the Other
Validation-Error Learning Curve
Choosing Between Bias, Variance, and Dataset Mismatch Fixes
Diagnosing High Avoidable Bias from Similar Error Rates
A Single Model Can Show Several Error Sources at Once
A Big Gap Between Training and Development Error Suggests Overfitting
Reading High Bias from Training and Dev Error
A Model Can Show Both Bias and Variance
Strong Classification Performance Comes from Low Bias and Low Variance
What are the two main sources of error in machine learning?
Why can it be useful to tell whether a model's main problem is bias or variance before deciding how to improve it?
In the usual pair of major supervised-learning error sources, bias and ____ go together.
Which pair names the two broad error categories used to guide model improvement?
When a model's errors are due to bias or variance, that information helps you decide whether collecting more labels is likely to help.
Major error sources in supervised learning
Match Each Error-Analysis Idea to Its Role
Use bias and variance to choose an improvement strategy.
How can knowing the bias and variance pattern of your model help you make better decisions?
Sources of Error in Machine Learning Models
Knowing bias and variance helps you decide whether _____ is worth the effort.
Match each child concept to the bias-variance idea it supports.
Order the steps for deciding whether more labeled data is the right fix for a classifier.
Using Bias and Variance to Choose the Next Improvement
Choosing an Error Check Before Expanding the Dataset
Why Diagnose Bias and Variance Before Choosing an Improvement Strategy?
Learn After
A classifier shows about 15% bias and 15% variance. Which description fits best?
True or False: When a classifier shows both high bias and high variance, the usual overfitting/underfitting labels do not cleanly describe the situation.
A classifier with high bias usually performs _____ on the training data.
Identify the Bias and Variance Pattern
Put the diagnostic steps in order for deciding whether a model has high bias, high variance, or both.
In the worked bias–variance diagnosis, what were the two estimated error rates?
True or False: A model that has both high bias and high variance can be overfitting and underfitting at the same time.
Equal Bias and Variance in a Fraud Detector
Match each model-performance pattern to the most likely diagnosis.
Reasoning About Bias and Variance From Training and Validation Results
Explain why a model can show both underfitting and overfitting at the same time.
Diagnosing a Fraud Model with 12% Training Error and 27% Dev Error
What happens when a model has both high bias and high variance?