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