@azure/arm-machinelearning

Account key datastore credentials configuration.

Datastore account key secrets.

A Machine Learning compute based on AKS.

Secrets related to a Machine Learning compute based on AKS.

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

Steady
Resizing

An Azure Machine Learning compute.

AML Token identity configuration.

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

Personal
Shared

Forecast horizon determined automatically by system.

AutoMLJob class. Use this class for executing AutoML tasks like Classification/Regression etc. See TaskType enum for all the tasks supported.

N-Cross validations determined automatically.

Target lags rolling window determined automatically.

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

None
Local
Remote

Azure Blob datastore configuration.

Azure Data Lake Gen1 datastore configuration.

Azure Data Lake Gen2 datastore configuration.

Azure File datastore configuration.

Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation

Batch inference settings per deployment.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

Contains response data for the listNext operation.

Contains response data for the list operation.

Contains response data for the update operation.

Batch endpoint configuration.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

Contains response data for the listKeys operation.

Contains response data for the listNext operation.

Contains response data for the list operation.

Contains response data for the update operation.

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

Info
Warning
Debug

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

SummaryOnly
AppendRow

Defines a Sampling Algorithm that generates values based on previous values

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

USD

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

None
ReadOnly
ReadWrite

Certificate datastore credentials configuration.

Datastore certificate secrets.

Classification task in AutoML Table vertical.

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

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.
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.
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.
BernoulliNaiveBayes: Naive Bayes classifier for multivariate Bernoulli models.
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.
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.
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.
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.
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.
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.
LightGBM: LightGBM is a gradient boosting framework that uses tree based learning algorithms.
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.
XGBoostClassifier: XGBoost: Extreme Gradient Boosting Algorithm. This algorithm is used for structured data where target column values can be divided into distinct class values.

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

AUCWeighted: AUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Accuracy: Accuracy is the ratio of predictions that exactly match the true class labels.
NormMacroRecall: Normalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
AveragePrecisionScoreWeighted: The arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
PrecisionScoreWeighted: The arithmetic mean of precision for each class, weighted by number of true instances in each class.
IOU: Intersection Over Union. Intersection of predictions divided by union of predictions.

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

AUCWeighted: AUC is the Area under the curve. This metric represents arithmetic mean of the score for each class, weighted by the number of true instances in each class.
Accuracy: Accuracy is the ratio of predictions that exactly match the true class labels.
NormMacroRecall: Normalized macro recall is recall macro-averaged and normalized, so that random performance has a score of 0, and perfect performance has a score of 1.
AveragePrecisionScoreWeighted: The arithmetic mean of the average precision score for each class, weighted by the number of true instances in each class.
PrecisionScoreWeighted: The arithmetic mean of precision for each class, weighted by number of true instances in each class.

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

FastProd
DenseProd
DevTest

Azure Resource Manager resource envelope.

Container for code asset versions.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

Contains response data for the listNext operation.

Contains response data for the list operation.

Azure Resource Manager resource envelope.

Code asset version details.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

Contains response data for the listNext operation.

Contains response data for the list operation.

Command job definition.

Command Job limit class.

Azure Resource Manager resource envelope.

Component container definition.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

Contains response data for the listNext operation.

Contains response data for the list operation.

Azure Resource Manager resource envelope.

Definition of a component version: defines resources that span component types.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

Contains response data for the listNext operation.

Contains response data for the list operation.

Contains response data for the createOrUpdate operation.

Contains response data for the get operation.

An Azure Machine Learning compute instance.

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

personal

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

Creating
CreateFailed
Deleting
Running
Restarting
JobRunning
SettingUp
SetupFailed
Starting
Stopped
Stopping
UserSettingUp
UserSetupFailed
Unknown
Unusable