AutoMLVerticalRegressionModel Struct

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

AutoMLVerticalRegressionModel.cs

Enum for all Regression models supported by AutoML.

public readonly struct AutoMLVerticalRegressionModel : IEquatable<Azure.ResourceManager.MachineLearning.Models.AutoMLVerticalRegressionModel>

type AutoMLVerticalRegressionModel = struct

Public Structure AutoMLVerticalRegressionModel
Implements IEquatable(Of AutoMLVerticalRegressionModel)

Inheritance

ValueType

AutoMLVerticalRegressionModel

Implements

IEquatable<AutoMLVerticalRegressionModel>

Constructors

AutoMLVerticalRegressionModel(String)

Initializes a new instance of AutoMLVerticalRegressionModel.

Properties

DecisionTree	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.
ElasticNet	Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions.
ExtremeRandomTrees	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.
GradientBoosting	The technique of transiting week learners into a strong learner is called Boosting. The gradient boosting algorithm process works on this theory of execution.
KNN	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.
LassoLars	Lasso model fit with Least Angle Regression a.k.a. Lars. It is a Linear Model trained with an L1 prior as regularizer.
LightGBM	LightGBM is a gradient boosting framework that uses tree based learning algorithms.
RandomForest	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.
SGD	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.
XGBoostRegressor	XGBoostRegressor: Extreme Gradient Boosting Regressor is a supervised machine learning model using ensemble of base learners.

Methods

Equals(AutoMLVerticalRegressionModel)	Indicates whether the current object is equal to another object of the same type.
ToString()	Returns the fully qualified type name of this instance.

Operators

Equality(AutoMLVerticalRegressionModel, AutoMLVerticalRegressionModel)	Determines if two AutoMLVerticalRegressionModel values are the same.
Implicit(String to AutoMLVerticalRegressionModel)	Converts a string to a AutoMLVerticalRegressionModel.
Inequality(AutoMLVerticalRegressionModel, AutoMLVerticalRegressionModel)	Determines if two AutoMLVerticalRegressionModel values are not the same.