Label encoding uses less memory than one-hot encoding, assigning a single numerical value to each category while maintaining data dimensionality. Unlike one-hot encoding, binary encoding produces ...

One-Hot Encoding represents categorical variables as binary vectors. Each category is underlined by a new column, represented by a binary vector with only one high (‘1’) and the rest low (‘0 ...

For instance, if we have a column of level in a dataset which includes beginners, intermediate and advanced. After applying the label encoder, it will be converted into 0,1 and 2 respectively. OneHot ...

Feature encoding is a process used to convert categorical data into a format that can be provided to machine learning algorithms to improve predictions. Two common methods of feature encoding are ...

Label Encoding assigns a unique integer to each category, while OneHot Encoding creates binary columns for each category, representing its presence or absence. Description: In this scenario, I ...

Most of the content on various social media platforms has enormous textual data. Before being used in machine learning models, this textual data must be transformed into numerical formats using ...