Label encoding in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is label encoding in machine learning?

Label encoding is a technique that converts categorical text data into numbers so that machine learning models can understand and use it.

beginner

Why do we need to use label encoding?

Because many machine learning models only work with numbers, label encoding changes categories like 'red', 'blue', 'green' into numbers like 0, 1, 2.

beginner

How does label encoding assign numbers to categories?

It assigns a unique integer to each category, usually starting from 0 and increasing by 1 for each new category.

intermediate

What is a potential problem with label encoding for some machine learning models?

Label encoding can make models think that one category is greater or less than another because of the numbers, which might not be true for categories without order.

beginner

Give an example of label encoding for the categories: ['cat', 'dog', 'bird'].

An example encoding could be: 'cat' → 0, 'dog' → 1, 'bird' → 2. Each category gets a unique number.

What does label encoding do to categorical data?

A Converts categories into numbers

B Removes categories from data

C Changes numbers into categories

D Splits data into training and testing sets

Label encoding converts categories into numbers so models can process them.

Which of these is a possible label encoding for ['apple', 'banana', 'cherry']?

A [0, 1, 2]

B [10, 20, 30]

C ['apple', 'banana', 'cherry']

D [1, 2, 3]

Label encoding usually starts from 0 and assigns increasing integers to categories.

Why might label encoding cause problems for some models?

A It removes important data

B It creates a false order between categories

C It changes numbers into text

D It duplicates categories

Label encoding can make models think categories have order when they don't.

Which type of data is label encoding used for?

A Image data

B Numerical continuous data

C Categorical data

D Audio data

Label encoding is used to convert categorical data into numbers.

What is the first number assigned in label encoding?

A 100

B -1

C 1

D 0

Label encoding usually starts numbering categories from 0.

Explain what label encoding is and why it is important in machine learning.

Think about how models understand data.

Describe a situation where label encoding might cause problems and why.

Consider how numbers might mislead a model.