ClassificationModel Struct

Definition

Namespace:

Azure.ResourceManager.MachineLearning.Models

Assembly:

Azure.ResourceManager.MachineLearning.dll

Package:

Azure.ResourceManager.MachineLearning v1.1.1

Source:

ClassificationModel.cs

Enum for all classification models supported by AutoML.

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

type ClassificationModel = struct

Public Structure ClassificationModel
Implements IEquatable(Of ClassificationModel)

Inheritance

ValueType

ClassificationModel

Implements

IEquatable<ClassificationModel>

Constructors

ClassificationModel(String)

Initializes a new instance of ClassificationModel.

Properties

BernoulliNaiveBayes	Naive Bayes classifier for multivariate Bernoulli models.
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.
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.
LightGBM	LightGBM is a gradient boosting framework that uses tree based learning algorithms.
LinearSVM	A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text. Linear SVM performs best when input data is linear, i.e., data can be easily classified by drawing the straight line between classified values on a plotted graph.
LogisticRegression	Logistic regression is a fundamental classification technique. It belongs to the group of linear classifiers and is somewhat similar to polynomial and linear regression. Logistic regression is fast and relatively uncomplicated, and it's convenient for you to interpret the results. Although it's essentially a method for binary classification, it can also be applied to multiclass problems.
MultinomialNaiveBayes	The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work.
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.
SVM	A support vector machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems. After giving an SVM model sets of labeled training data for each category, they're able to categorize new text.
XGBoostClassifier	XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.

Methods

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