Flow
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)

Visibility: public Uploaded 07-11-2019 by Continuous Integration sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 716 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_3634 study_3989 study_1397 study_1420 study_1505 study_1525 study_1545 study_1610 study_1630 study_1652 study_1696 study_1716 study_1739 study_1783 study_1824 study_1877 study_1894 study_1953 study_1999 study_2019 study_2060 study_2080 study_2146 study_2229 study_2312 study_2404 study_2439 study_2480 study_2572 study_2600 study_2640 study_2682 study_2729 study_2752 study_2772 study_2795 study_2842 study_2888 study_2908 study_2965 study_2982 study_3030 study_3052 study_3098 study_3118 study_3135 study_3157 study_3179 study_3199 study_3239 study_3304 study_3348 study_3368 study_3400 study_3421 study_3443 study_3463 study_3485 study_3505 study_3548 study_3583 study_3687 study_3812 study_3839 study_3859 study_3881 study_3901 study_3941 study_3977 study_4068 study_4112 study_4147 study_4164 study_4226 study_4249 study_4293 study_4313 study_4336 study_4397 study_4543 study_4563 study_4614 study_4631 study_4654 study_4672 study_4694 study_4717 study_4754 study_4813 study_4854 study_4871 study_4891 study_4911 study_4949 study_4993 study_5036 study_5098 study_5142 study_5183 study_5407 study_5661 study_5696 study_5737 study_5973 study_6020 study_6055 study_6072 study_6092 study_6154 study_6192 study_6236 study_6304 study_6324 study_6380 study_6397 study_6417 study_6455 study_6475 study_6516 study_6536 study_6562 study_6600 study_6623 study_6646 study_6669 study_6749 study_6787 study_6825 study_6836 study_6862 study_6885 study_6902 study_6940 study_6981 study_6998 study_7021 study_7041 study_7082 study_7102 study_7119 study_7136 study_7153 study_7224 study_7241 study_7261 study_7278 study_7295 study_7330 study_7347 study_7382 study_7399 study_7416 study_7433 study_7453 study_7491 study_7541 study_7603 study_7635 study_10112 study_10168 study_10182 study_10253 study_10273 study_10341 study_10376 study_10411 study_10612 study_10655 study_10741 study_10755 study_10844 study_10849 study_10893 study_10931 study_10966 study_11010 study_11030 study_11068 study_11200 study_11238 study_11267 study_11302 study_11374 study_11409 study_11426 study_11461 study_11553 study_11607 study_11624 study_11644 study_11661 study_11685 study_11762 study_11792 study_11812 study_11829 study_11846 study_11863 study_11895 study_11927 study_11962 study_12081 study_12143 study_12196 study_12216 study_12252 study_12358 study_12393 study_12450 study_12467 study_12571 study_12588 study_12623 study_12665 study_12703 study_12720 study_12758 study_12832 study_12849 study_12914 study_12979 study_13009 study_13026 study_13074 study_13133 study_13153 study_13282 study_13323 study_13370
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": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
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