fetch_haberman_survival#

mlquantify.datasets.fetch_haberman_survival(*, data_home=None, download_if_missing=True, return_X_y=False, as_frame=False, n_retries=3, delay=1.0, protocol=None, n_samples=1000, sample_size=500, random_state=None, target_col=None)[source]#

Haberman survival after breast-cancer surgery (binary, hard).

306 patients from the 1958-1970 Haberman study, described by three integer features (age at operation, year of operation, number of positive axillary nodes). The label is 5-year survival. Classes overlap heavily, so AUC is low and quantifier adjustment matters.

Quantification: heavily overlapping classes + tiny sample -> stresses ACC/PACC adjustment.

Samples

306

Features

3 (integer)

Classes

2 (survived 73.5% / died 26.5%)

Missing

0

Source: https://archive.ics.uci.edu/dataset/43/haberman+s+survival

Parameters:
data_homestr or path-like, default=None

Folder used to cache the downloaded file(s); defaults to _data/ next to the package.

download_if_missingbool, default=True

If False, raise instead of downloading when the cache is empty.

return_X_ybool, default=False

Return (X, y) instead of a Bunch.

as_framebool, default=False

Return .data as a DataFrame, .target as a Series, and a combined .frame (features + a "target" column).

n_retriesint, default=3

Number of download attempts before giving up.

delayfloat, default=1.0

Seconds to wait between attempts.

protocol{None, “app”, “npp”, “upp”, “ppp”} or mlquantify protocol, default=None

If set, draw evaluation sample-bags with an mlquantify protocol; the Bunch then also has .samples (index bags into .data), .prevalences and .protocol.

n_samplesint, default=1000

Number of prevalence points (bags) generated by the protocol.

sample_sizeint, default=500

Instances per bag (the protocol batch_size).

random_stateint or None, default=None

Seed forwarded to the protocol.

Returns:
dataBunch

Dictionary-like object. Attributes: data (features), target (labels), feature_names, target_names, DESCR; frame when as_frame=True; and samples / prevalences / protocol when protocol is set.

(X, y)tuple

Returned instead when return_X_y=True.

References

Haberman, S. (1976). UCI ML Repository (#43).

Examples

>>> b = fetch_haberman_survival(); b.data.shape  
(306, 3)