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:

The category Initialize Regression Model includes the following modules:

Module

Description

Bayesian Linear Regression

Creates a Bayesian linear regression model

Boosted Decision Tree Regression

Creates a regression model using the Boosted Decision Tree algorithm

Decision Forest Regression

Creates a regression model using the decision forest algorithm

Fast Forest Quantile Regression

Creates a quantile regression model

Linear Regression

Creates a linear regression model

Neural Network Regression

Creates a regression model using a neural network algorithm

Ordinal Regression

Creates an ordinal regression model

Poisson Regression

Creates a regression model that assumes data has a Poisson distribution

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