Algorithm & module reference for Azure Machine Learning designer

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

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 pipeline in the designer, 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 preparation:
Data input and output Move data from cloud sources into your pipeline. Write your results or intermediate data to Azure Storage, a SQL database, or Hive, while running a pipeline, or use cloud storage to exchange data between pipelines. Enter Data Manually
Export Data
Import Data
Data transformation Operations on data that are unique to machine learning, such as normalizing or binning data, dimensionality reduction, and converting data among various file formats. Add Columns
Add Rows
Apply Math Operation
Apply SQL Transformation
Clean Missing Data
Clip Values
Convert to CSV
Convert to Dataset
Edit Metadata
Join Data
Normalize Data
Partition and Sample
Remove Duplicate Rows
Select Columns Transform
Select Columns in Dataset
Split Data
Feature Selection Select a subset of relevant, useful features to use in building an analytical model. Filter Based Feature Selection
Permutation Feature Importance
Statistical Functions Provide a wide variety of statistical methods related to data science. Summarize Data
Machine learning algorithms:
Regression Predict a value. Boosted Decision Tree Regression
Decision Forest Regression
Linear Regression
Neural Network Regression
Clustering Group data together. K-Means Clustering
Classification Predict a class. Choose from binary (two-class) or multiclass algorithms. Multiclass Boosted Decision Tree
Multiclass Decision Forest
Multiclass Logistic Regression
Multiclass Neural Network
One vs. All Multiclass
Two-Class Averaged Perceptron
Two-Class Boosted Decision Tree
Two-Class Decision Forest
Two-Class Logistic Regression
Two-Class Neural Network
Two Class Support Vector Machine
Build and evaluate models:
Model training Run data through the algorithm. Train Clustering Model
Train Model
Tune Model Hyperparameters
Model Scoring and Evaluation Measure the accuracy of the trained model. Apply Transformation
Assign Data to Clusters
Cross Validate Model
Evaluate Model
Score Model
Python language Write code and embed it in a module to integrate Python with your pipeline. Create Python Model
Execute Python Script
R language Write code and embed it in a module to integrate R with your pipeline. Execute R Script
Text Analytics Provide specialized computational tools for working with both structured and unstructured text. Extract N Gram Features from Text
Feature Hashing
Preprocess Text
Recommendation Build recommendation models. Evaluate Recommender
Score SVD Recommender
Train SVD Recommender

Error messages

Learn about the error messages and exception codes you might encounter using modules in Azure Machine Learning designer.