Plumbing for all learners (classifiers and regressions)
Functions
accepts_dataset_as_samples(fx) | Decorator to extract samples from Datasets. |
accepts_samples_as_dataset(fx) | Decorator to wrap samples into a Dataset. |
deepcopy(x[, memo, _nil]) | Deep copy operation on arbitrary Python objects. |
idhash(val) | Craft unique id+hash for an object |
is_datasetlike(obj) | Check if an object looks like a Dataset. |
Classes
AttributeMap([map, mapnumeric, ...]) | Map to translate literal values to numeric ones (and back). |
Classifier([space]) | Abstract classifier class to be inherited by all classifiers .. |
ConditionalAttribute([enabled]) | Simple container intended to conditionally store the value .. |
ConfusionMatrix([labels, labels_map]) | Class to contain information and display confusion matrix. |
Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
Learner([auto_train, force_train]) | Common trainable processing object. |
Measure([null_dist]) | A measure computed from a Dataset |
Parameter(default[, constraints, ro, index, ...]) | This class shall serve as a representation of a parameter. |
RegressionStatistics(**kwargs) | Class to contain information and display on regression results. |
Exceptions
AttributeMap([map, mapnumeric, ...]) | Map to translate literal values to numeric ones (and back). |
Classifier([space]) | Abstract classifier class to be inherited by all classifiers .. |
ConditionalAttribute([enabled]) | Simple container intended to conditionally store the value .. |
ConfusionMatrix([labels, labels_map]) | Class to contain information and display confusion matrix. |
Dataset(samples[, sa, fa, a]) | Generic storage class for datasets with multiple attributes. |
Learner([auto_train, force_train]) | Common trainable processing object. |
Measure([null_dist]) | A measure computed from a Dataset |
Parameter(default[, constraints, ro, index, ...]) | This class shall serve as a representation of a parameter. |
RegressionStatistics(**kwargs) | Class to contain information and display on regression results. |