Label encoding in Data Analysis Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is label encoding in data science?

Label encoding is a technique to convert categorical text data into numbers. Each unique category is assigned a unique integer. This helps machine learning models understand the data.

beginner

Why do we use label encoding instead of leaving categories as text?

Most machine learning models work with numbers, not text. Label encoding changes categories into numbers so models can process them.

beginner

Which Python library provides a simple way to do label encoding?

The scikit-learn library provides the LabelEncoder class to easily convert categories into numbers.

intermediate

What is a potential problem with label encoding for categorical variables with no order?

Label encoding can create a false sense of order or priority between categories because numbers imply ranking, which may mislead some models.

intermediate

How can you convert encoded labels back to original categories?

Using the inverse_transform method of LabelEncoder, you can convert numbers back to their original category names.

What does label encoding do to categorical data?

A Splits categories into multiple columns

B Removes categories from the data

C Changes numbers into categories

D Converts categories into unique numbers

Label encoding assigns a unique number to each category.

Which Python class is commonly used for label encoding?

A PCA

B LabelEncoder

C StandardScaler

D OneHotEncoder

LabelEncoder from scikit-learn is used for label encoding.

What is a risk of using label encoding on unordered categories?

A It may imply a false order

B It removes categories

C It duplicates data

D It converts numbers to text

Numbers assigned can wrongly suggest order or priority.

How can you revert encoded labels back to original categories?

A Using inverse_transform method

B Using fit_transform method

C Using transform method

D Using to_categorical method

inverse_transform converts numbers back to original labels.

Why do machine learning models need label encoding?

A They ignore categorical data

B They prefer text over numbers

C They only understand numbers, not text

D They automatically convert text

Models require numeric input, so text categories must be encoded.

Explain what label encoding is and why it is used in data science.

Think about how computers understand data.

Describe a limitation of label encoding and how it might affect a machine learning model.

Consider what numbers might wrongly suggest about categories.