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A native Pulumi package for creating and managing Azure resources.

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// *** WARNING: this file was generated by pulumi-java-gen. ***
// *** Do not edit by hand unless you're certain you know what you are doing! ***

package com.pulumi.azurenative.machinelearningservices.enums;

import com.pulumi.core.annotations.EnumType;
import java.lang.String;
import java.util.Objects;
import java.util.StringJoiner;

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

        private final String value;

        BlockedTransformers(String value) {
            this.value = Objects.requireNonNull(value);
        }

        @EnumType.Converter
        public String getValue() {
            return this.value;
        }

        @Override
        public java.lang.String toString() {
            return new StringJoiner(", ", "BlockedTransformers[", "]")
                .add("value='" + this.value + "'")
                .toString();
        }
    }