Classifier
ProximityStump
Proximity Stump class.
Model a decision stump which uses a distance measure to partition data.
Quickstart
python
from sktime.classification.distance_based import ProximityStump
estimator = ProximityStump(random_state=None, distance_measure=None, verbosity=0, n_jobs=1)Tags
Capabilities
- Multivariate: Not supported
- Probabilistic prediction: Not supported
- Multiple outputs: Not supported
- Unequal-length series: Not supported
- Missing values: Not supported
- Feature importance: Not supported
- Training-set estimate: Not supported
- Time-limited training: Not supported
Parameters(4)
- random_state: integer, the random state
- distance_measure: ``None`` (default) or str; if str, one of
“euclidean”, “dtw”, “ddtw”, “wdtw”, “wddtw”, “msm”, “lcss”, “erp” distance measure to use if
None, selects distances randomly from the list of available distances- verbosity: logging verbosity
- n_jobs: number of jobs to run in parallel *across threads”
Examples
>>> from sktime.classification.distance_based import ProximityStump
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> clf = ProximityStump ()
>>> clf. fit (X_train, y_train) ProximityStump(
... )
>>> y_pred = clf. predict (X_test)