26046 1159 TEST60fa2bb50esklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier) sklearn.Pipeline(SimpleImputer,DummyClassifier) sklearn.pipeline.Pipeline 1 openml==0.14.1,sklearn==0.23.1 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``. 2024-01-15T10:51:12 English sklearn==0.23.1 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 memory str or object with the joblib null 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 ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming steps list [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}] List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator verbose bool false If True, the time elapsed while fitting each step will be printed as it is completed. imputer 26047 1159 TEST60fa2bb50esklearn.impute._base.SimpleImputer sklearn.SimpleImputer sklearn.impute._base.SimpleImputer 1 openml==0.14.1,sklearn==0.23.1 Imputation transformer for completing missing values. 2024-01-15T10:51:12 English sklearn==0.23.1 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 add_indicator boolean false If True, a :class:`MissingIndicator` transform will stack onto output of the imputer's transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won't appear on the missing indicator even if there are missing values at transform/test time. copy boolean true If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if `copy=False`: - If X is not an array of floating values; - If X is encoded as a CSR matrix; - If add_indicator=True fill_value string or numerical value null When strategy == "constant", fill_value is used to replace all occurrences of missing_values If left to the default, fill_value will be 0 when imputing numerical data and "missing_value" for strings or object data types missing_values number NaN The placeholder for the missing values. All occurrences of `missing_values` will be imputed. For pandas' dataframes with nullable integer dtypes with missing values, `missing_values` should be set to `np.nan`, since `pd.NA` will be converted to `np.nan` strategy string "mean" The imputation strategy - If "mean", then replace missing values using the mean along each column. Can only be used with numeric data - If "median", then replace missing values using the median along each column. Can only be used with numeric data - If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data - If "constant", then replace missing values with fill_value. Can be used with strings or numeric data .. versionadded:: 0.20 strategy="constant" for fixed value imputation verbose integer 0 Controls the verbosity of the imputer openml-python python scikit-learn sklearn sklearn_0.23.1 classifier 26048 1159 TEST60fa2bb50esklearn.dummy.DummyClassifier sklearn.DummyClassifier sklearn.dummy.DummyClassifier 1 openml==0.14.1,sklearn==0.23.1 DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems. 2024-01-15T10:51:12 English sklearn==0.23.1 numpy>=1.13.3 scipy>=0.19.1 joblib>=0.11 threadpoolctl>=2.0.0 constant int or str or array null The explicit constant as predicted by the "constant" strategy. This parameter is useful only for the "constant" strategy. random_state int null Controls the randomness to generate the predictions when ``strategy='stratified'`` or ``strategy='uniform'`` Pass an int for reproducible output across multiple function calls See :term:`Glossary <random_state>` strategy str "prior" Strategy to use to generate predictions * "stratified": generates predictions by respecting the training set's class distribution * "most_frequent": always predicts the most frequent label in the training set * "prior": always predicts the class that maximizes the class prior (like "most_frequent") and ``predict_proba`` returns the class prior * "uniform": generates predictions uniformly at random * "constant": always predicts a constant label that is provided by the user. This is useful for metrics that evaluate a non-majority class .. versionchanged:: 0.22 The default value of `strategy` will change to "prior" in version 0.24. Starting from version 0.22, a warning will be raised if `strategy` is not explicitly set .. versionadded:: 0.17 Dummy Classifier now supports prior fitting strategy using parameter *prior* openml-python python scikit-learn sklearn sklearn_0.23.1 openml-python python scikit-learn sklearn sklearn_0.23.1