Models LibsvmModel

---

class LibsvmModel

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

Support Vector Machine [R9] 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 LIBSVM [R10] [R11] implementation of SVM. The LIBSVM model is initialized, trained on columns of a frame, used to predict the labels of observations in a frame and used to test 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.

[R9]	https://en.wikipedia.org/wiki/Support_vector_machine

[R10]

https://www.csie.ntu.edu.tw/~cjlin/libsvm/

[R11]

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

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] New frame with new predicted label column.
publish(self)	[BETA] Creates a scoring engine tar file.
score(self, vector)	[ALPHA] Calculate the prediction label for a single observation.
test(self, frame, label_column[, observation_columns])	[ALPHA] Predict test frame labels and return metrics.
train(self, frame, label_column, observation_columns[, svm_type, ...])	[ALPHA] Train LIBSVM 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 [R12] 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 LIBSVM [R13] [R14] implementation of SVM. The LIBSVM model is initialized, trained on columns of a frame, used to predict the labels of observations in a frame and used to test 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.

[R12]

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

[R13]

https://www.csie.ntu.edu.tw/~cjlin/libsvm/

[R14]

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