Basic Bias and Variance Remedies
A simple way to respond to these errors is to make the model more flexible when it is underfitting, and to use more training data when it is overfitting.
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Architectural Changes Can Lower Bias and Variance Together
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
Learn After
Why enlarging a model can lower bias
Why Increasing Model Size and Data Eventually Stops Helping
Methods to Reduce High Avoidable Bias
What should you do when avoidable bias is high?
True or false: When a model has high variance, adding more training examples is often a useful remedy.
For persistent underfitting, increase the _____ of the model.
Match each learning problem or model change with the usual remedy or meaning.
Order the steps for applying a simple model-fix rule.
How the diagnosis determines the fix for bias and variance
Select the right remedy for bias and variance in two projects.
State the recommended fix for each training problem.
Which option applies the rule of thumb correctly to both cases?
Bias and variance call for the same fix
Methods for Reducing High Variance in Machine-Learning Models