Error rate and failure analysis in Agentic AI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the error rate in machine learning?

The error rate is the percentage of wrong predictions made by a model compared to the total predictions. It shows how often the model makes mistakes.

beginner

Why is failure analysis important in machine learning?

Failure analysis helps us understand why a model makes mistakes. It identifies patterns or reasons behind errors so we can improve the model.

beginner

How do you calculate the error rate from predictions?

Error rate = (Number of wrong predictions) ÷ (Total predictions). For example, if 10 out of 100 predictions are wrong, error rate = 10%.

intermediate

What is a common method to perform failure analysis?

A common method is to look at the confusion matrix to see which types of errors happen most, then analyze those cases to find causes.

intermediate

How can failure analysis improve a machine learning model?

By finding error patterns, we can fix data issues, adjust model settings, or add features. This reduces errors and makes the model better.

What does a high error rate indicate about a model?

A The model makes many mistakes

B The model is very accurate

C The model never makes mistakes

D The model is overfitting perfectly

A high error rate means the model often predicts wrong answers.

Which tool helps identify types of errors in classification?

A Histogram

B Confusion matrix

C Scatter plot

D Line chart

A confusion matrix shows counts of correct and wrong predictions by class.

What is the first step in failure analysis?

A Look at error patterns

B Train a new model

C Ignore errors

D Increase dataset size

First, we examine where and how errors happen to understand causes.

If a model has 5 wrong predictions out of 50, what is the error rate?

A 10%

B 15%

C 5%

D 20%

Error rate = 5 ÷ 50 = 0.1 or 10%.

How can failure analysis help improve a model?

A By removing all features

B By ignoring errors

C By reducing dataset size

D By finding error causes and fixing them

Understanding errors helps us fix problems and improve model accuracy.

Explain what error rate means and how you calculate it.

Think about how many predictions are wrong out of total.

Describe the steps and purpose of failure analysis in machine learning.

Why do we look closely at mistakes?