361 1159 TEST5659a58a3fsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier) sklearn.Pipeline(StandardScaler,DummyClassifier) sklearn.pipeline.Pipeline 1 openml==0.14.1,sklearn==1.3.2 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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl... 2024-01-10T15:41:57 English sklearn==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 threadpoolctl>=2.0.0 memory str or object with the joblib null Used to cache the fitted transformers of the pipeline. The last step will never be cached, even if it is a transformer. 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 of tuple [{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "dummy", "step_name": "dummy"}}] List of (name, transform) tuples (implementing `fit`/`transform`) that are chained in sequential order. The last transform must be an estimator verbose bool false If True, the time elapsed while fitting each step will be printed as it is completed. scaler 362 1159 TEST5659a58a3fsklearn.preprocessing._data.StandardScaler sklearn.StandardScaler sklearn.preprocessing._data.StandardScaler 1 openml==0.14.1,sklearn==1.3.2 Standardize features by removing the mean and scaling to unit variance. The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if `with_mean=False`, and `s` is the standard deviation of the training samples or one if `with_std=False`. Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using :meth:`transform`. Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance). For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 ... 2024-01-10T15:41:57 English sklearn==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 threadpoolctl>=2.0.0 copy bool true If False, try to avoid a copy and do inplace scaling instead This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned with_mean bool false If True, center the data before scaling This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory with_std bool true If True, scale the data to unit variance (or equivalently, unit standard deviation). openml-python python scikit-learn sklearn sklearn_1.3.2 dummy 363 1159 TEST5659a58a3fsklearn.dummy.DummyClassifier sklearn.DummyClassifier sklearn.dummy.DummyClassifier 1 openml==0.14.1,sklearn==1.3.2 DummyClassifier makes predictions that ignore the input features. This classifier serves as a simple baseline to compare against other more complex classifiers. The specific behavior of the baseline is selected with the `strategy` parameter. All strategies make predictions that ignore the input feature values passed as the `X` argument to `fit` and `predict`. The predictions, however, typically depend on values observed in the `y` parameter passed to `fit`. Note that the "stratified" and "uniform" strategies lead to non-deterministic predictions that can be rendered deterministic by setting the `random_state` parameter if needed. The other strategies are naturally deterministic and, once fit, always return the same constant prediction for any value of `X`. 2024-01-10T15:41:57 English sklearn==1.3.2 numpy>=1.17.3 scipy>=1.5.0 joblib>=1.1.1 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 "prior" openml-python python scikit-learn sklearn sklearn_1.3.2 openml-python python scikit-learn sklearn sklearn_1.3.2