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Choosing Between Bias, Variance, and Dataset Mismatch Fixes
A model improvement plan should depend on the dominant source of error. If the main issue is avoidable bias, you would use techniques that make the model fit the training problem better. If the main issue is high variance, you would use methods that improve generalization. Error estimates can also help reveal whether a train/dev mismatch is the real obstacle, so the next step is to target the right kind of change instead of applying generic fixes.
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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
Architectural Changes Can Lower Bias and Variance Together
Basic Bias and Variance Remedies
Changing Architecture Can Shift Both Error Sources
Why is it useful to diagnose which source of error is larger in an ML project?
Methods that lower bias in a model will always lower its variance too.
Building intuition about _____ and variance helps you decide which change will improve a model.
Match each observation or action to the error issue it mainly helps diagnose or improve.
Determine the Main Source of Error in a Machine Learning Model
Which two error rates are most useful for diagnosing bias and variance?
Reviewing your model's error patterns can help you decide whether to focus on data mismatch.
Bias and variance require different remedies
Match each error pattern to the most likely model issue.
Order the reasoning steps for deciding whether bias or variance is the bigger issue.
Using Error Breakdown to Decide What to Fix First
First Priority for a Fraud Detector with High Underfitting
Using Error Diagnosis to Set the Next Priority