A-Z List of Machine Learning Studio Modules

 

Published: August 25, 2015

Updated: June 2, 2017

This topic provides an alphabetized list of the modules provided in Azure Machine Learning Studio Machine Learning Studio.

The modules in Azure Machine Learning modules cover a wide range of features and functions necessary for machine learning tasks, including data conversion and transformation functions, modules for executing R or Python script, and a variety of algorithms such as decision trees and decision forests, clustering, time series, recommendation models, and anomaly detection.

ModuleDescription
Add ColumnsAdds a set of columns from one dataset to another
Add RowsAppends a set of rows from an input dataset to the end of another dataset
Apply FilterApplies a filter to specified columns of a dataset
Apply Math OperationApplies a mathematical operation to column values
Apply SQL TransformationRuns a SQLite query on input datasets to transform the data
Apply TransformationApplies a well-specified data transformation to a dataset
Assign to Clusters (deprecated)Assigns data to clusters using an existing trained clustering model

This module has been deprecated, but is available for use with existing experiments. For new experiments, use Assign Data to Clusters.
Assign Data to ClustersAssigns data to clusters using an existing trained clustering model
Bayesian Linear RegressionCreates a Bayesian linear regression model
Boosted Decision Tree RegressionCreates a regression model using the Boosted Decision Tree algorithm
Build Count Table (deprecated)Creates counts to use in building features
Build Counting TransformCreates counts to use in building features
Clean Missing DataSpecifies how to handle the values missing from a dataset
Clip ValuesDetects outliers and then clips or replaces their values
Compute Elementary StatisticsCalculates specified summary statistics for selected dataset columns
Detect LanguagesDetects the language of each line in the input file
Compute Linear CorrelationCalculates the linear correlation between column values in a dataset
Convert To ArffConverts data input to the attribute relation file format used by the Weka toolset
Convert to CSVConverts data input to a comma-separated values format
Convert to DatasetConverts data input to the internal Dataset format used by Microsoft Azure Machine Learning
Convert to Indicator ValuesConverts categorical values in columns to indicator values
Convert to SVMLightConverts data input to the format used by the SVM-Light framework
Convert to TSVConverts data input to the tab-delimited format
Count Featurizer (deprecated)Generates a set of compact features based on a count table
Create R ModelCreates an R model using custom resources
Cross-Validate ModelCross-validates parameter estimates for classification or regression models by partitioned the data
Decision Forest RegressionCreates a regression model using the decision forest algorithm
Detect LanguagesDetects the language of each line in the input file
Edit MetadataEdits metadata associated with columns in a dataset
Enter Data ManuallyEnables entering and editing small datasets by typing values
Evaluate ModelEvaluates a scored classification or regression model with standard metrics
Evaluate Probability FunctionFits a specified probability distribution function to a dataset
Evaluate RecommenderEvaluates the accuracy of recommender model predictions
Execute Python ScriptExecutes a Python script from an Azure Machine Learning experiment
Execute R ScriptExecutes an R script from an Azure Machine Learning experiment
Export Count TableExports counts from a count transform
Export DataWrites a dataset to web URLs or to various forms of cloud-based storage in Azure, such as tables, blobs, and Azure SQL databases

This module was formerly named Writer.
Extract Key Phrases from TextExtracts key words and phrases from a text column
Extract N-Gram Features from TextCreates N-Gram dictionary features and does feature selection on them
Fast Forest Quantile RegressionCreates a quantile regression model
Feature HashingConverts text data to integer-encoded features using the Vowpal Wabbit library
Filter Based Feature SelectionIdentifies the features in a dataset with the greatest predictive power
FIR FilterCreates a finite impulse response filter for signal processing
Fisher Linear Discriminant AnalysisIdentifies the linear combination of feature variables that can best group data into separate classes
Group Categorical ValuesGroups data from multiple categories into a new category
Group Data into BinsPuts numerical data into bins
IIR FilterCreates an infinite impulse response filter for signal processing
Import Count TableImports counts from an existing count table
Import DataLoads data from external sources on the web; from various forms of cloud-based storage in Azure such as tables, blobs, and SQL databases; and from on-premises SQL Server databases

This module was formerly named Reader.
Import ImagesLoads images from Azure BLOB storage into a dataset
Join DataJoins two datasets
K-Means ClusteringConfigures and initializes a K-means clustering model
Latent Dirichlet AllocationPerforms topic modeling using the Vowpal Wabbit library for LDA
Linear RegressionCreates a linear regression model
Load Trained ModelGets a trained model so that you can use it in an experiment for scoring
Median FilterCreates a median filter used to smooth data for trend analysis
Merge Count TransformMerges two sets of count tables
Missing Values Scrubber (deprecated)Specifies how to handle values that are missing from a dataset
Modify Count Table ParametersBuilds a compact set of count-based features from count tables
Moving Average FilterCreates a moving average filter that smooths data for trend analysis
Multiclass Decision ForestCreates a multiclass classification model using the decision forest algorithm
Multiclass Decision JungleCreates a multiclass classification model using the decision jungle algorithm
Multiclass Logistic RegressionCreates a multiclass logistic regression classification model
Multiclass Neural NetworkCreates a multiclass classification model using a neural network algorithm
Named Entity RecognitionRecognizes named entities in a text column
Neural Network RegressionCreates a regression model using a neural network algorithm
Normalize DataRescales numeric data to constrain dataset values to a standard range
One-Class Support Vector MachineCreates a one-class Support Vector Machine model for anomaly detection
One-vs-All MulticlassCreates a multiclass classification model from an ensemble of binary classification models
Ordinal RegressionCreates an ordinal regression model
Partition and SampleCreates multiple partitions of a dataset based on sampling
Permutation Feature ImportanceComputes the permutation feature importance scores of feature variables given a trained model and a test dataset
PCA-Based Anomaly DetectionCreates an anomaly detection model using Principal Component Analysis
Poisson RegressionCreates a regression model that assumes data has a Poisson distribution
Preprocess TextPerforms cleaning operations on text
Pretrained Cascade Image ClassificationCreates a pretrained image classification model for frontal faces using the OpenCV Library
Principal Component AnalysisComputes a set of features with reduced dimensionality for more efficient learning
Remove Duplicate RowsRemoves the duplicate rows from a dataset
Replace Discrete ValuesReplaces discrete values from one column with numeric values based on another column
Score Matchbox RecommenderScores predictions for a dataset using the Matchbox recommender
Score ModelScores predictions for a trained classification or regression model
Score Vowpal Wabbit 7-4 ModelScores data using the Vowpal Wabbit machine learning system

Requires a trained model built using VW versions 7-4 and 7-6
Score Vowpal Wabbit 7-10 ModelScores data using the Vowpal Wabbit machine learning system

Requires a trained model built using VW version 7-10
Score Vowpal Wabbit 8 ModelScores data using the Vowpal Wabbit machine learning system from the command line interface

Requires a trained model built using VW version 8
Select Columns in DatasetSelects columns to include or exclude from a dataset in an operation
SMOTEIncreases the number of low incidence examples in a dataset using synthetic minority oversampling
Split DataPartitions the rows of a dataset into two distinct sets
Summarize DataGenerates a basic descriptive statistics report for the columns in a dataset
Sweep ClusteringPerforms a parameter sweep on a clustering model to determine the optimum parameter settings
Test Hypothesis Using t-TestCompares means from two datasets using a t-test
Threshold FilterCreates a threshold filter that constrains values
Time Series Anomaly DetectionLearns a trend in time series data and uses it to detect anomalies
Train Anomaly Detection ModelTrains an anomaly detector model and labels data from the training set
Train Clustering ModelTrains a clustering model and assigns data from the training set to clusters
Train Matchbox RecommenderTrains a Bayesian recommender using the Matchbox algorithm
Train ModelTrains a classification or regression model in a supervised manner
Train Vowpal Wabbit 7-4 ModelTrains a model from the Vowpal Wabbit machine learning system

This module is for compatibility with VW versions 7-4 and 7-6
Train Vowpal Wabbit 7-10 ModelTrains a model from the Vowpal Wabbit machine learning system

This module is for the current VW version, 7-10
Train Vowpal Wabbit 8 ModelTrains a model using version 8 of the Vowpal Wabbit machine learning system

This module is for the VW version 8
Tune Model HyperparametersPerforms a parameter sweep on a regression or classification model to determine the optimum parameter settings
Two-Class Averaged PerceptronCreates an averaged perceptron binary classification model
Two-Class Bayes Point MachineCreates a Bayes point machine binary classification model
Two-Class Boosted Decision TreeCreates a binary classifier using a boosted decision tree algorithm
Two-Class Decision ForestCreates a two-class classification model using the decision forest algorithm
Two-Class Decision JungleCreates a two-class classification model using the decision jungle algorithm
Two-Class Locally Deep Support Vector MachineCreates a binary classification model using the locally deep Support Vector Machine algorithm
Two-Class Logistic RegressionCreates a two-class logistic regression model
Two-Class Neural NetworkCreates a binary classifier using a neural network algorithm
Two-Class Support Vector MachineCreates a binary classification model using the Support Vector Machine algorithm
Unpack Zipped DatasetsUnpacks datasets from a zip package in user storage
User-Defined FilterCreates a custom finite or infinite impulse response filter

Module Categories and Descriptions
Module Data Types

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