ChebyshevOverSamplerยถ
Over-sampling for imbalanced regression using Chebyshev's inequality.
Chebyshev's inequality can be used to define the probability of target observations being frequent values (w.r.t. the distribution mean).
Let
Taking
Alternatively, one can use
Parametersยถ
-
regressor (base.Regressor)
The regression model that will receive the biased sample.
Examplesยถ
>>> from river import datasets
>>> from river import evaluate
>>> from river import imblearn
>>> from river import metrics
>>> from river import preprocessing
>>> from river import rules
>>> model = (
... preprocessing.StandardScaler() |
... imblearn.ChebyshevOverSampler(
... regressor=rules.AMRules(
... n_min=50, delta=0.01
... )
... )
... )
>>> evaluate.progressive_val_score(
... datasets.TrumpApproval(),
... model,
... metrics.MAE(),
... print_every=500
... )
[500] MAE: 1.152726
[1,000] MAE: 0.954873
MAE: 0.954049
Methodsยถ
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
learn_one
Fits to a set of features x
and a real-valued target y
.
Parameters
- x
- y
- kwargs
Returns
self
predict_one
Predicts the target value of a set of features x
.
Parameters
- x
Returns
The prediction.
Referencesยถ
-
Aminian, Ehsan, Rita P. Ribeiro, and Joรฃo Gama. "Chebyshev approaches for imbalanced data streams regression models." Data Mining and Knowledge Discovery 35.6 (2021): 2389-2466. โฉ