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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 Regression models supported by AutoML.
     * 
     */
    @EnumType
    public enum RegressionModels {
        /**
         * Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
         * 
         */
        ElasticNet("ElasticNet"),
        /**
         * The technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
         * 
         */
        GradientBoosting("GradientBoosting"),
        /**
         * Decision Trees are a non-parametric supervised learning method used for both classification and regression tasks.
         * The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
         * 
         */
        DecisionTree("DecisionTree"),
        /**
         * K-nearest neighbors (KNN) algorithm uses 'feature similarity' to predict the values of new datapoints
         * which further means that the new data point will be assigned a value based on how closely it matches the points in the training set.
         * 
         */
        KNN("KNN"),
        /**
         * Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
         * 
         */
        LassoLars("LassoLars"),
        /**
         * SGD: Stochastic gradient descent is an optimization algorithm often used in machine learning applications
         * to find the model parameters that correspond to the best fit between predicted and actual outputs.
         * It's an inexact but powerful technique.
         * 
         */
        SGD("SGD"),
        /**
         * Random forest is a supervised learning algorithm.
         * The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method.
         * The general idea of the bagging method is that a combination of learning models increases the overall result.
         * 
         */
        RandomForest("RandomForest"),
        /**
         * Extreme Trees is an ensemble machine learning algorithm that combines the predictions from many decision trees. It is related to the widely used random forest algorithm.
         * 
         */
        ExtremeRandomTrees("ExtremeRandomTrees"),
        /**
         * LightGBM is a gradient boosting framework that uses tree based learning algorithms.
         * 
         */
        LightGBM("LightGBM"),
        /**
         * XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.
         * 
         */
        XGBoostRegressor("XGBoostRegressor");

        private final String value;

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

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

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