CovertMark.analytics.learning module

class CovertMark.analytics.learning.Classifier(classifier)

Bases: abc.ABC

Abstract class for common classifier methods.

predict(validation_inputs)

Classify unseen inputs, can be used for both validation prediction and recall prediction.

Parameters

validation_inputs (list) – input rows of features for validation, should not have been seen during training.

Returns

array of positive(1) / negative(0) labels predicted.

train(training_features, training_labels)

Train on training input feature rows.

Parameters

• training_features (list) – input rows of features for training, without labels. These should be redrawn between training runs.

• training_labels (list) – corresponding labels for the input rows.

class CovertMark.analytics.learning.LogisticRegression(multithreaded=True)

Bases: CovertMark.analytics.learning.Classifier

Generic logistic regression with stable L1 penalisation and fast SAGA solver to converge quickly, but can overfit on high dimensional spaces.

class CovertMark.analytics.learning.SGD(loss='hinge', multithreaded=True)

Bases: CovertMark.analytics.learning.Classifier

Stochastic gradient descent linear classification, as a less memory-intensive and incremental learning-compatible alternative to linear SVM (LinearSVC).