Error classification in GraphQL - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is error classification in data science?

Error classification is the process of identifying and categorizing different types of errors that occur in data analysis or machine learning models, such as false positives, false negatives, or misclassifications.

beginner

What is a false positive error?

A false positive error happens when a model incorrectly predicts a positive outcome for a case that is actually negative. For example, a spam filter marking a normal email as spam.

beginner

What is a false negative error?

A false negative error happens when a model incorrectly predicts a negative outcome for a case that is actually positive. For example, a medical test missing a disease that is present.

intermediate

Why is it important to classify errors in machine learning?

Classifying errors helps us understand where a model makes mistakes, so we can improve it. It also helps us decide which errors are more costly and need more attention.

intermediate

What is the difference between accuracy and error classification?

Accuracy measures how often a model is correct overall, while error classification breaks down the types of mistakes the model makes, like false positives and false negatives.

Which of the following is a false positive error?

A Predicting normal for a spam email

B Predicting spam for a normal email

C Predicting disease when no disease is present

D Predicting no disease when disease is present

A false positive is when the model predicts a positive outcome incorrectly, like marking a normal email as spam or predicting disease when no disease is present.

What does a false negative error mean?

A Model predicts negative but actual is positive

B Model predicts positive but actual is negative

C Model predicts positive and actual is positive

D Model predicts negative and actual is negative

A false negative means the model missed a positive case by predicting negative.

Why do we classify errors in machine learning?

A To understand and improve model performance

B To ignore mistakes

C To increase the number of errors

D To make the model slower

Classifying errors helps us understand where the model fails and how to improve it.

Which error type is more critical in a medical diagnosis model?

A False positive

B False negative

C True positive

D True negative

False negatives are critical because missing a disease can have serious consequences.

Accuracy tells us:

A Only false positives

B Types of errors the model makes

C How often the model is correct overall

D Only false negatives

Accuracy measures the overall correctness of the model's predictions.

Explain the difference between false positive and false negative errors with simple examples.

Think about spam emails and medical tests.

Why is error classification important when evaluating a machine learning model?

Consider how different errors affect decisions.