Machine Learning Pipeline

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What is the purpose of encoding categorical features in machine learning?

To convert categorical features into numerical features that can be processed by machine learning algorithms

What is the difference between ordinal and nominal categories?

Ordinal categories are ordered, while nominal categories are unordered

How does LabelEncoder encode target labels?

It assigns a value between 0 and n_classes-1 to each target label

What is the limitation of using LabelEncoder to encode categorical features?

It only works on one feature at a time Signup and view all the answers

How does OrdinalEncoder differ from LabelEncoder?

OrdinalEncoder can encode multiple categorical features at once, and it returns a single column of integers for each feature Signup and view all the answers

What is the problem with encoding categorical features using integers?

The ordering and size of the integers are meaningless Signup and view all the answers

What is one-hot-encoding, and how does it differ from encoding categorical features with integers?

One-hot-encoding is a technique that converts categorical features into multiple binary features, whereas encoding with integers assigns a single integer value to each category Signup and view all the answers