- imblearn.pipeline.make_pipeline(*steps, memory=None, verbose=False)[source]#
Construct a Pipeline from the given estimators.
This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.
- *stepslist of estimators
A list of estimators.
- memoryNone, str or object with the joblib.Memory interface, default=None
Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute
stepsto inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.
- verbosebool, default=False
If True, the time elapsed while fitting each step will be printed as it is completed.
Returns an imbalanced-learn
Pipelineinstance that handles samplers.
Class for creating a pipeline of transforms with a final estimator.
>>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())])
Multiclass classification with under-sampling
Example of topic classification in text documents
Customized sampler to implement an outlier rejections estimator
Benchmark over-sampling methods in a face recognition task
Fitting model on imbalanced datasets and how to fight bias
Compare sampler combining over- and under-sampling
Evaluate classification by compiling a report
Metrics specific to imbalanced learning
Compare over-sampling samplers
Usage of pipeline embedding samplers
Compare under-sampling samplers