BlockedTransformers type

Defines values for BlockedTransformers.
KnownBlockedTransformers can be used interchangeably with BlockedTransformers, this enum contains the known values that the service supports.

Known values supported by the service

TextTargetEncoder: Target encoding for text data.
OneHotEncoder: Ohe hot encoding creates a binary feature transformation.
CatTargetEncoder: Target encoding for categorical data.
TfIdf: Tf-Idf stands for, term-frequency times inverse document-frequency. This is a common term weighting scheme for identifying information from documents.
WoETargetEncoder: 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.
LabelEncoder: Label encoder converts labels/categorical variables in a numerical form.
WordEmbedding: Word embedding helps represents words or phrases as a vector, or a series of numbers.
NaiveBayes: Naive Bayes is a classified that is used for classification of discrete features that are categorically distributed.
CountVectorizer: Count Vectorizer converts a collection of text documents to a matrix of token counts.
HashOneHotEncoder: Hashing One Hot Encoder can turn categorical variables into a limited number of new features. This is often used for high-cardinality categorical features.