Models SvmModel

---

class SvmModel

Create a ‘new’ instance of a Support Vector Machine model.

Support Vector Machine [R66] is a supervised algorithm used to perform binary classification. A Support Vector Machine constructs a high dimensional hyperplane which is said to achieve a good separation when a hyperplane has the largest distance to the nearest training-data point of any class. This model runs the MLLib implementation of SVM [R67] with SGD [R68] optimizer. The SVMWithSGD model is initialized, trained on columns of a frame, used to predict the labels of observations in a frame, and tests the predicted labels against the true labels. During testing, labels of the observations are predicted and tested against the true labels using built-in binary Classification Metrics.

footnotes

[R66]

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

[R67]

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

[R68]

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

Attributes

name	Set or get the name of the model object.

Methods

__init__(self[, name, _info])	[ALPHA] Create a ‘new’ instance of a Support Vector Machine model.
predict(self, frame[, observation_columns])	[ALPHA] Make new frame with additional column for predicted label.
test(self, frame, label_column[, observation_columns])	[ALPHA] Predict test frame labels and return metrics.
train(self, frame, label_column, observation_columns[, intercept, ...])	[ALPHA] Train SVM model.

__init__(self, name=None)

[ALPHA] Create a ‘new’ instance of a Support Vector Machine model.

Parameters:

name : unicode (default=None)

User supplied name.

Returns:

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

Support Vector Machine [R69] is a supervised algorithm used to perform binary classification. A Support Vector Machine constructs a high dimensional hyperplane which is said to achieve a good separation when a hyperplane has the largest distance to the nearest training-data point of any class. This model runs the MLLib implementation of SVM [R70] with SGD [R71] optimizer. The SVMWithSGD model is initialized, trained on columns of a frame, used to predict the labels of observations in a frame, and tests the predicted labels against the true labels. During testing, labels of the observations are predicted and tested against the true labels using built-in binary Classification Metrics.

footnotes

[R69]

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

[R70]

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

[R71]

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