# regression¶

The GraphLab Create regression toolkit contains models for regression problems. Currently, we support linear regression and boosted trees. In addition to these models, we provide a smart interface that selects the right model based on the data. If you are unsure about which model to use, simply use create() function.

Training data must contain a column for the ‘target’ variable and one or more columns representing feature variables.

# Set up the data
>>> import graphlab as gl
>>> data =  gl.SFrame('https://static.turi.com/datasets/regression/houses.csv')

# Select the best model based on your data.
>>> model = gl.regression.create(data, target='price',
...                                  features=['bath', 'bedroom', 'size'])

# Make predictions and evaluate results.
>>> predictions = model.predict(data)
>>> results = model.evaluate(data)


## creating a regression model¶

 regression.create Automatically create a suitable regression model based on the provided training data.

## random forest¶

 random_forest_regression.create Create a RandomForestRegression to predict a scalar target variable using one or more features. random_forest_regression.get_default_options Get the default options for the toolkit RandomForestRegression. random_forest_regression.RandomForestRegression Encapsulates random forest models for regression tasks.

## decision tree¶

 decision_tree_regression.create Create a DecisionTreeRegression to predict a scalar target variable using one or more features. decision_tree_regression.get_default_options Get the default options for the toolkit DecisionTreeRegression. decision_tree_regression.DecisionTreeRegression The prediction is based on a collection of base learners, regression trees.

## boosted trees¶

 boosted_trees_regression.create Create a BoostedTreesRegression to predict a scalar target variable using one or more features. boosted_trees_regression.get_default_options Get the default options for the toolkit BoostedTreesRegression. boosted_trees_regression.BoostedTreesRegression Encapsulates gradient boosted trees for regression tasks.

## linear regression¶

 linear_regression.create Create a LinearRegression to predict a scalar target variable as a linear function of one or more features. linear_regression.get_default_options Get the default options for the toolkit LinearRegression. linear_regression.LinearRegression Linear regression is an approach for modeling a scalar target $$y$$ as a linear function of one or more explanatory variables denoted $$X$$.