Machine Learning / Initialize Model / Regression
Updated: July 1, 2015
What is Regression?
Regression algorithms are algorithms that learn to predict the value of a real function for a single instance of data. Regression algorithms can incorporate input from multiple features, by determining the contribution of each feature of the data to the regression function.
Once the regression algorithm has trained a function based on already labeled data, the function can be used to predict the label of a new (unlabeled) instance. For example, a housing price predictor might use a regression algorithm to predict the value of a particular house, based on historical data about regional house prices.
Wondering how to select a regression algorithm? See these topics:
Machine learning algorithm cheat sheet for Azure ML
Provides a graphical decision chart to guide you through the selection process
How to choose Azure Machine Learning algorithms for clustering, classification, or regression
Explains in greater detail the different types of machine learning algorithms and how they're used
The category Initialize Regression Model includes the following modules:
Module | Description |
|---|---|
Creates a Bayesian linear regression model | |
Creates a regression model using the Boosted Decision Tree algorithm | |
Creates a regression model using the decision forest algorithm | |
Creates a quantile regression model | |
Creates a linear regression model | |
Creates a regression model using a neural network algorithm | |
Creates an ordinal regression model | |
Creates a regression model that assumes data has a Poisson distribution |