LogisticRegressionModel new

---

__init__(self, name=None)

Create a ‘new’ instance of logistic regression model.

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

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

Logistic Regression [R35] is a widely used supervised binary and multi-class classification algorithm. The Logistic Regression model is initialized, trained on columns of a frame, predicts the labels of observations, and tests the predicted labels against the true labels. This model runs the MLLib implementation of Logistic Regression [R36], with enhanced features — trained model summary statistics; Covariance and Hessian matrices; ability to specify the frequency of the train and test observations. Testing performance can be viewed via built-in binary and multi-class Classification Metrics. It also allows the user to select the optimizer to be used - L-BFGS [R37] or SGD [R38].

footnotes

[R35]

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

[R36]

https://spark.apache.org/docs/1.3.0/mllib-linear-methods.html#logistic-regression

[R37]

https://en.wikipedia.org/wiki/Limited-memory_BFGS

[R38]

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