KnownBlockedTransformers enum

Target encoding for categorical data.

Count Vectorizer converts a collection of text documents to a matrix of token counts.

Hashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.

Label encoder converts labels/categorical variables in a numerical form.

Naive Bayes is a classified that is used for classification of discrete features that are categorically distributed.

Ohe hot encoding creates a binary feature transformation.

Target encoding for text data.

Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.

Weight of Evidence encoding is a technique used to encode categorical variables. It uses the natural log of the P(1)/P(0) to create weights.

Word embedding helps represents words or phrases as a vector, or a series of numbers.