Models LinearRegressionModel

---

class LinearRegressionModel

Create a ‘new’ instance of a Linear Regression model.

Linear Regression [R18] is used to model the relationship between a scalar dependent variable and one or more independent variables. The Linear Regression model is initialized, trained on columns of a frame and used to predict the value of the dependent variable given the independent observations of a frame. This model runs the MLLib implementation of Linear Regression [R19] with the SGD [R20] optimizer.

footnotes

[R18]

https://en.wikipedia.org/wiki/Linear_regression

[R19]

https://spark.apache.org/docs/1.3.0/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression

[R20]

https://en.wikipedia.org/wiki/Stochastic_gradient_descent

Attributes

name	Set or get the name of the model object.

Methods

__init__(self[, name, _info])	Create a ‘new’ instance of a Linear Regression model.
predict(self, frame[, observation_columns])	[ALPHA] Make new frame with column for label prediction.
train(self, frame, label_column, observation_columns[, intercept, ...])	[ALPHA] Build linear regression model.

__init__(self, name=None)

Create a ‘new’ instance of a Linear Regression model.

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

: <bound method AtkEntityType.__name__ of <trustedanalytics.rest.jsonschema.AtkEntityType object at 0x7f9e68702090>>

Linear Regression [R21] is used to model the relationship between a scalar dependent variable and one or more independent variables. The Linear Regression model is initialized, trained on columns of a frame and used to predict the value of the dependent variable given the independent observations of a frame. This model runs the MLLib implementation of Linear Regression [R22] with the SGD [R23] optimizer.

footnotes

[R21]

https://en.wikipedia.org/wiki/Linear_regression

[R22]

https://spark.apache.org/docs/1.3.0/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression

[R23]

https://en.wikipedia.org/wiki/Stochastic_gradient_descent