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Bias (Informal Definition)
Informally, bias is a model's error on the training set when the training set is very large. It gives a rough sense of how much error remains even after the model has access to plenty of data.
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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
High Training Error Calls for Better Fit, Not More Data
Low Bias and Low Variance Mean Good Performance
A Negative Gap to the Best Achievable Error Usually Signals Memorization
Informal and Technical Uses of Bias and Variance
Training Performance Is a Baseline for Held-Out Performance
Informal Meaning of Model Bias
True or False: In this framework, the term bias refers to a model’s error on the validation or test set.
Informal meaning of bias in a large-sample setting
Informal Meaning of Bias
In this informal usage, bias means the model's error on the training set.
Informally, an algorithm's _____ is the error rate on the training set.
Match each learning-curve term to its meaning.
Order the steps for estimating bias from training error.
Why use a very large training set when estimating bias?
Bias Is Measured on the Holdout Set
In the large-sample limit, bias is typically assessed on the _____ data.
Match each learning-term description with its role in the informal bias definition.
Order the steps for deciding whether bias is the main cause of poor model performance.
How Informal Bias Relates to Training Error
Interpreting Training Error on a Large Dataset
Informal Bias and Training Error