Flow
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier))

sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier))

Visibility: public Uploaded 24-11-2022 by Continuous Integration sklearn==0.21.2 numpy>=1.11.0 scipy>=0.17.0 joblib>=0.11 11 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_0.21.2 study_259 study_646 study_676 study_754 study_938 study_955 study_983 study_994 study_1004 study_1007 study_1012 study_1017 study_1058 study_1068 study_1070 study_1081 study_1095 study_1108 study_1125 study_1140 study_1168 study_1176 study_1201 study_1206 study_1224 study_1234 study_1239 study_1249 study_1250 study_1254 study_1265 study_1280 study_1290 study_1297 study_1311 study_1312 study_1325 study_1344 study_1354 study_1357 study_1367 study_1402 study_1418 study_1420 study_1423 study_1425 study_1433 study_1439 study_1447 study_1453 study_1454 study_1460 study_1467 study_1475 study_1503 study_1512 study_1513 study_1515 study_1522 study_1523 study_1533 study_1534 study_1549 study_1565 study_1574 study_1584 study_1597 study_1617 study_1631 study_1638 study_1644 study_1655 study_1658 study_1659 study_1664 study_1691 study_1738 study_1765 study_1771 study_1773 study_1786 study_1793 study_1794 study_1798 study_1800 study_1810 study_1831 study_1836 study_1841 study_1850 study_1851 study_1856 study_1867 study_1876 study_1882 study_1893 study_1894 study_1903 study_1908 study_1940 study_1945 study_1948 study_1991 study_2031 study_2299
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.

Parameters

memoryUsed 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 ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consumingdefault: null
stepsList of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimatordefault: [{"oml-python:serialized_object": "component_reference", "value": {"key": "enc", "step_name": "enc"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "rs", "step_name": "rs"}}]
verboseIf True, the time elapsed while fitting each step will be printed as it is completed.default: false

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table