Models LinearRegressionModel

---

class LinearRegressionModel

Entity LinearRegressionModel

Attributes

last_read_date	Read-only property - Last time this model’s data was accessed.
name	Set or get the name of the model object.
status	Read-only property - Current model life cycle status.

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:

: Model

A new instance of LinearRegressionModel

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/1.5.0/mllib-linear-methods.html#linear-least-squares-lasso-and-ridge-regression

[R23]

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

Examples

Consider the following model trained and tested on the sample data set in frame ‘frame’.

Consider the following frame containing two columns.

>>> frame.inspect()
[#]  x1   y
==============
[0]  0.0   0.0
[1]  1.0   2.5
[2]  2.0   5.0
[3]  3.0   7.5
[4]  4.0  10.0
[5]  5.0  12.5
[6]  6.0  15.0
[7]  7.0  17.5
[8]  8.0  20.0
[9]  9.0  22.5

>>> model = ta.LinearRegressionModel()
[===Job Progress===]
>>> model.train(frame, "y", ["x1"], intercept=True, num_iterations=100)
[===Job Progress===]
>>> predicted_frame = model.predict(frame, ["x1"])
[===Job Progress===]
>>> predicted_frame.inspect()
[#]  x1   y     predicted_value
==================================
[0]  0.0   0.0  -3.74940273364e+55
[1]  1.0   2.5  -2.72603544372e+56
[2]  2.0   5.0  -5.07713061407e+56
[3]  3.0   7.5  -7.42822578442e+56
[4]  4.0  10.0  -9.77932095478e+56
[5]  5.0  12.5  -1.21304161251e+57
[6]  6.0  15.0  -1.44815112955e+57
[7]  7.0  17.5  -1.68326064658e+57
[8]  8.0  20.0  -1.91837016362e+57
[9]  9.0  22.5  -2.15347968065e+57