# Algorithm & component reference for Azure Machine Learning designer

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

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

For help with choosing algorithms, see

Tip

In any pipeline in the designer, you can get information about a specific component. Select the **Learn more** link in the component card when hovering on the component in the component list, or in the right pane of the component.

## Data preparation components

Functionality | Description | component |
---|---|---|

Data Input and Output | Move data from cloud sources into your pipeline. Write your results or intermediate data to Azure Storage, or SQL Database, 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 Convert to Indicator Values Edit Metadata Group Data into Bins Join Data Normalize Data Partition and Sample Remove Duplicate Rows SMOTE Select Columns Transform Select Columns in Dataset Split Data |

Feature Selection | Select a subset of relevant, useful features to use to build 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

Functionality | Description | component |
---|---|---|

Regression | Predict a value. | Boosted Decision Tree Regression Decision Forest Regression Fast Forest Quantile Regression Linear Regression Neural Network Regression Poisson 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 One vs. One 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 |

## Components for building and evaluating models

## Web service

Learn about the web service components, which are necessary for real-time inference in Azure Machine Learning designer.

## Error messages

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

## Next steps

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