17862 12269 sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,step_1=sklearn.svm._classes.SVC) sklearn.Pipeline(SelectPercentile,SVC) sklearn.pipeline.Pipeline 1 openml==0.10.2,sklearn==0.22.1 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``. 2020-05-19T03:15:16 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 memory None 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": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}] 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. step_1 17707 12269 sklearn.svm._classes.SVC sklearn.SVC sklearn.svm._classes.SVC 4 openml==0.10.2,sklearn==0.22.1 C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of samples. For large datasets consider using :class:`sklearn.svm.LinearSVC` or :class:`sklearn.linear_model.SGDClassifier` instead, possibly after a :class:`sklearn.kernel_approximation.Nystroem` transformer. The multiclass support is handled according to a one-vs-one scheme. For details on the precise mathematical formulation of the provided kernel functions and how `gamma`, `coef0` and `degree` affect each other, see the corresponding section in the narrative documentation: :ref:`svm_kernels`. 2020-05-18T19:46:42 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 C float 4.3827607743865347e-07 Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty break_ties bool false If true, ``decision_function_shape='ovr'``, and number of classes > 2, :term:`predict` will break ties according to the confidence values of :term:`decision_function`; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict .. versionadded:: 0.22 cache_size float 200 Specify the size of the kernel cache (in MB) class_weight : {dict, 'balanced'}, optional Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` class_weight null coef0 float -15.332261879793727 Independent term in kernel function It is only significant in 'poly' and 'sigmoid' decision_function_shape 'ovo' "ovr" Whether to return a one-vs-rest ('ovr') decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one ('ovo') decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one ('ovo') is always used as multi-class strategy .. versionchanged:: 0.19 decision_function_shape is 'ovr' by default .. versionadded:: 0.17 *decision_function_shape='ovr'* is recommended .. versionchanged:: 0.17 Deprecated *decision_function_shape='ovo' and None* degree int 3 Degree of the polynomial kernel function ('poly') Ignored by all other kernels gamma : {'scale', 'auto'} or float, optional (default='scale') Kernel coefficient for 'rbf', 'poly' and 'sigmoid' - if ``gamma='scale'`` (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma, - if 'auto', uses 1 / n_features .. versionchanged:: 0.22 The default value of ``gamma`` changed from 'auto' to 'scale' gamma 0.0010017639673572045 kernel string "sigmoid" Specifies the kernel type to be used in the algorithm It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or a callable If none is given, 'rbf' will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape ``(n_samples, n_samples)`` max_iter int -1 Hard limit on iterations within solver, or -1 for no limit probability boolean true Whether to enable probability estimates. This must be enabled prior to calling `fit`, will slow down that method as it internally uses 5-fold cross-validation, and `predict_proba` may be inconsistent with `predict`. Read more in the :ref:`User Guide <scores_probabilities>` random_state int 42 The seed of the pseudo random number generator used when shuffling the data for probability estimates. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. shrinking boolean false Whether to use the shrinking heuristic tol float 0.00013562683316384962 Tolerance for stopping criterion verbose bool false Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context openml-python python scikit-learn sklearn sklearn_0.22.1 step_0 17776 12269 sklearn.feature_selection._univariate_selection.SelectPercentile sklearn.SelectPercentile sklearn.feature_selection._univariate_selection.SelectPercentile 1 openml==0.10.2,sklearn==0.22.1 Select features according to a percentile of the highest scores. 2020-05-18T23:59:48 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 percentile int 82.42842516532346 Percent of features to keep. score_func callable {"oml-python:serialized_object": "function", "value": "sklearn.feature_selection._mutual_info.mutual_info_classif"} Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores Default is f_classif (see below "See also"). The default function only works with classification tasks openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1