BlockedTransformers

Target encoding for text data.

Ohe hot encoding creates a binary feature transformation.

Target encoding for categorical 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.

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

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

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

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.

Returns a representation of an immutable list of all enum entries, in the order they're declared.

Returns the enum constant of this type with the specified name. The string must match exactly an identifier used to declare an enum constant in this type. (Extraneous whitespace characters are not permitted.)

Returns an array containing the constants of this enum type, in the order they're declared.