Machine Learning Pipeline

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

Ordinal categories are ordered, while nominal categories are unordered

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

<p>It only works on one feature at a time</p> Signup and view all the answers

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

<p>The ordering and size of the integers are meaningless</p> Signup and view all the answers

<p>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</p> Signup and view all the answers

<p>When the categorical features have no inherent ordering or relationship</p> Signup and view all the answers

<p>It preserves the ordering of the categories</p> Signup and view all the answers

<p>It can handle multiple categorical features at once, and preserves the ordering of ordinal categories</p> Signup and view all the answers