predict¶

k_fold_predict(dataset, extractor, classifier, *, k, random_state=None, sampling_function, balance_sample_weights=True, log)¶

Run k-fold cross-validation on a dataset.

Parameters:

dataset (AnnotatedDataset) – The dataset to cross-validate.
extractor (BaseExtractor[TypeVar(F, bound= Shaped)]) – The extractor to use.
classifier (Classifier) – The classifier to use.
k (int) – The number of folds.
random_state (int | Generator | None, default: None) – The random state to use.
sampling_function (SamplingFunction) – The sampling function to use.
balance_sample_weights (bool, default: True) – Whether to balance sample weights.
log (Logger | None) – The logger to use.

Return type:

DatasetClassificationResult

Returns:

The dataset classification result.

predict(sampleable, classifier, extractor, *, encoding_function=None, categories=None, exclude=None, log=None)¶

Run classification on a sampleable object.

Parameters:

sampleable (SampleableMixin) – The sampleable object to classify.
classifier (Classifier) – The classifier to use.
extractor (BaseExtractor[TypeVar(F, bound= Shaped)]) – The extractor to use.
encoding_function (EncodingFunction | None, default: None) – The encoding function to use.
categories (Iterable[str] | None, default: None) – The categories to use.
exclude (Iterable[str | int | tuple[str | int, str | int]] | None, default: None) – The identifiers to exclude.
log (Logger | None, default: None) – The logger to use.

Return type:

Returns:

The classification result.