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 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 an pipeline, or use cloud storage to exchange data between pipelines. Import Data
Enter Data Manually
Export 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
Clean Missing Data
Convert to CSV
Convert to Dataset
Edit Metadata
Join Data
Normalize Data
Remove Duplicate Rows
Select Columns Transform
Select Columns in Dataset
Sampling Split your data into one or more subsets subsets to train and test machine learning models. Cross Validate Model
Partition and Sample
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
Python and R Write code and embed it in a module to integrate Python and R with your pipeline. Create Python Model
Execute Python Script
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
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
One vs. All Multiclass
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. Boosted Decision Tree Regression
Decision Forest Regression
Linear Regression
Neural Network Regression
Recommender Build recommendation models. Evaluate Recommender
Score SVD Recommender
Train SVD Recommender
Build and evaluate models:
Train Run data through the algorithm. Train Model
Train Clustering Model
Tune Model Hyperparameters
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
Statistical Functions Provide a wide variety of statistical methods related to data science. Apply Math Operation
Summarize Data

Error messages

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