Algorithm & module reference overview

This reference content provides the technical background on each of the machine learning algorithms and modules available in the visual interface (preview) of Azure Machine Learning service.

Each module represents a set of code that can run independently and perform a machine learning task, given the required inputs. A module might contain a particular algorithm, or perform a task that is important in machine learning, such as missing value replacement, or statistical analysis.


In any experiment in the visual interface, you can get information about a specific module. Select the module, then select the more help link in the Quick Help pane.


Modules are organized by functionality:

Functionality Description Module
Data format conversions Convert data among various file formats used in machine learning, Convert to CSV
Data input and output Move data from cloud sources into your experiment. Write your results or intermediate data to Azure Storage, a SQL database, or Hive, while running an experiment, or use cloud storage to exchange data between experiments. Import Data
Export Data
Enter Data Manually
Data transformation Operations on data that are unique to machine learning, such as normalizing or binning data, feature selection, and dimensionality reduction. Select Columns in Dataset
Edit Metadata
Clean Missing Data
Feature Hashing
Extract N Gram Features from Text
Add Columns
Add Rows
Remove Duplicate Rows
Preprocess Text
Join Data
Split Data
Normalize Data
Partition and Sample
Python and R modules Write code and embed it in a module to integrate Python and R with your experiment. Execute Python Script
Create Python Model
Execute R Script
Machine learning algorithms:
Classification Predict a class. Choose from binary (two-class) or multiclass algorithms. Multiclass Decision Forest
Multiclass Boosted Decision Tree
Multiclass Logistic Regression
Multiclass Neural Network
Two-Class Logistic Regression
Two-Class Averaged Perceptron
Two-Class Boosted Decision Tree
Two-Class Decision Forest
Two-Class Neural Network
Two‑Class Support Vector Machine
Clustering Group data together. K-Means Clustering
Regression Predict a value. Linear Regression
Neural Network Regression
Decision Forest Regression
Boosted Decision Tree Regression
Build and evaluate models:
Train Run data through the algorithm. Train Model
Train Clustering Model
Evaluate Model Measure the accuracy of the trained model. Evaluate Model
Score Get predictions from the model you've just trained. Apply Transformation
Assign Data to Clusters
Score Model

Error messages

Learn about the error messages and exception codes you might encounter using modules in the visual interface of Azure Machine Learning service.