{"flow":{"id":"19007","uploader":"27568","name":"sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing._data.StandardScaler,svc=sklearn.svm._classes.SVC)","custom_name":"sklearn.Pipeline(StandardScaler,SVC)","class_name":"sklearn.pipeline.Pipeline","version":"4","external_version":"openml==0.12.2,sklearn==0.24.2","description":"Pipeline of transforms with a final estimator.\n\nSequentially apply a list of transforms and a final estimator.\nIntermediate steps of the pipeline must be 'transforms', that is, they\nmust implement fit and transform methods.\nThe final estimator only needs to implement fit.\nThe transformers in the pipeline can be cached using ``memory`` argument.\n\nThe purpose of the pipeline is to assemble several steps that can be\ncross-validated together while setting different parameters.\nFor this, it enables setting parameters of the various steps using their\nnames and the parameter name separated by a '__', as in the example below.\nA step's estimator may be replaced entirely by setting the parameter\nwith its name to another estimator, or a transformer removed by setting\nit to 'passthrough' or ``None``.","upload_date":"2021-09-01T16:02:17","language":"English","dependencies":"sklearn==0.24.2\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"memory","data_type":"str or object with the joblib","default_value":"null","description":"Used to cache the fitted transformers of the pipeline. By default,\n no caching is performed. If a string is given, it is the path to\n the caching directory. Enabling caching triggers a clone of\n the transformers before fitting. Therefore, the transformer\n instance given to the pipeline cannot be inspected\n directly. Use the attribute ``named_steps`` or ``steps`` to\n inspect estimators within the pipeline. Caching the\n transformers is advantageous when fitting is time consuming"},{"name":"steps","data_type":"list","default_value":"[{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"StandardScaler\", \"step_name\": \"StandardScaler\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"svc\", \"step_name\": \"svc\"}}]","description":"List of (name, transform) tuples (implementing fit\/transform) that are\n chained, in the order in which they are chained, with the last object\n an estimator"},{"name":"verbose","data_type":"bool","default_value":"false","description":"If True, the time elapsed while fitting each step will be printed as it\n is completed."}],"component":[{"identifier":"svc","flow":{"id":"19000","uploader":"27568","name":"sklearn.svm._classes.SVC","custom_name":"sklearn.SVC","class_name":"sklearn.svm._classes.SVC","version":"9","external_version":"openml==0.12.2,sklearn==0.24.2","description":"C-Support Vector Classification.\n\nThe implementation is based on libsvm. The fit time scales at least\nquadratically with the number of samples and may be impractical\nbeyond tens of thousands of samples. For large datasets\nconsider using :class:`~sklearn.svm.LinearSVC` or\n:class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a\n:class:`~sklearn.kernel_approximation.Nystroem` transformer.\n\nThe multiclass support is handled according to a one-vs-one scheme.\n\nFor details on the precise mathematical formulation of the provided\nkernel functions and how `gamma`, `coef0` and `degree` affect each\nother, see the corresponding section in the narrative documentation:\n:ref:`svm_kernels`.","upload_date":"2021-08-27T07:20:52","language":"English","dependencies":"sklearn==0.24.2\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"C","data_type":"float","default_value":"3.25","description":"Regularization parameter. The strength of the regularization is\n inversely proportional to C. Must be strictly positive. The penalty\n is a squared l2 penalty\n\nkernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'\n Specifies the kernel type to be used in the algorithm\n It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or\n a callable\n If none is given, 'rbf' will be used. If a callable is given it is\n used to pre-compute the kernel matrix from data matrices; that matrix\n should be an array of shape ``(n_samples, n_samples)``"},{"name":"break_ties","data_type":"bool","default_value":"false","description":"If true, ``decision_function_shape='ovr'``, and number of classes > 2,\n :term:`predict` will break ties according to the confidence values of\n :term:`decision_function`; otherwise the first class among the tied\n classes is returned. Please note that breaking ties comes at a\n relatively high computational cost compared to a simple predict\n\n .. versionadded:: 0.22"},{"name":"cache_size","data_type":"float","default_value":"200","description":"Specify the size of the kernel cache (in MB)"},{"name":"class_weight","data_type":"dict or","default_value":"null","description":"Set the parameter C of class i to class_weight[i]*C for\n SVC. If not given, all classes are supposed to have\n weight one\n The \"balanced\" mode uses the values of y to automatically adjust\n weights inversely proportional to class frequencies in the input data\n as ``n_samples \/ (n_classes * np.bincount(y))``"},{"name":"coef0","data_type":"float","default_value":"0.0","description":"Independent term in kernel function\n It is only significant in 'poly' and 'sigmoid'"},{"name":"decision_function_shape","data_type":[],"default_value":"\"ovr\"","description":[]},{"name":"degree","data_type":"int","default_value":"3","description":"Degree of the polynomial kernel function ('poly')\n Ignored by all other kernels\n\ngamma : {'scale', 'auto'} or float, default='scale'\n Kernel coefficient for 'rbf', 'poly' and 'sigmoid'\n\n - if ``gamma='scale'`` (default) is passed then it uses\n 1 \/ (n_features * X.var()) as value of gamma,\n - if 'auto', uses 1 \/ n_features\n\n .. versionchanged:: 0.22\n The default value of ``gamma`` changed from 'auto' to 'scale'"},{"name":"gamma","data_type":[],"default_value":"\"scale\"","description":[]},{"name":"kernel","data_type":[],"default_value":"\"poly\"","description":[]},{"name":"max_iter","data_type":"int","default_value":"-1","description":"Hard limit on iterations within solver, or -1 for no limit\n\ndecision_function_shape : {'ovo', 'ovr'}, default='ovr'\n Whether to return a one-vs-rest ('ovr') decision function of shape\n (n_samples, n_classes) as all other classifiers, or the original\n one-vs-one ('ovo') decision function of libsvm which has shape\n (n_samples, n_classes * (n_classes - 1) \/ 2). However, one-vs-one\n ('ovo') is always used as multi-class strategy. The parameter is\n ignored for binary classification\n\n .. versionchanged:: 0.19\n decision_function_shape is 'ovr' by default\n\n .. versionadded:: 0.17\n *decision_function_shape='ovr'* is recommended\n\n .. versionchanged:: 0.17\n Deprecated *decision_function_shape='ovo' and None*"},{"name":"probability","data_type":"bool","default_value":"false","description":"Whether to enable probability estimates. This must be enabled prior\n to calling `fit`, will slow down that method as it internally uses\n 5-fold cross-validation, and `predict_proba` may be inconsistent with\n `predict`. Read more in the :ref:`User Guide `"},{"name":"random_state","data_type":"int","default_value":"null","description":"Controls the pseudo random number generation for shuffling the data for\n probability estimates. Ignored when `probability` is False\n Pass an int for reproducible output across multiple function calls\n See :term:`Glossary `."},{"name":"shrinking","data_type":"bool","default_value":"true","description":"Whether to use the shrinking heuristic\n See the :ref:`User Guide `"},{"name":"tol","data_type":"float","default_value":"0.001","description":"Tolerance for stopping criterion"},{"name":"verbose","data_type":"bool","default_value":"false","description":"Enable verbose output. Note that this setting takes advantage of a\n per-process runtime setting in libsvm that, if enabled, may not work\n properly in a multithreaded context"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.24.2"]}},{"identifier":"StandardScaler","flow":{"id":"19003","uploader":"27568","name":"sklearn.preprocessing._data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing._data.StandardScaler","version":"9","external_version":"openml==0.12.2,sklearn==0.24.2","description":"Standardize features by removing the mean and scaling to unit variance\n\nThe standard score of a sample `x` is calculated as:\n\n z = (x - u) \/ s\n\nwhere `u` is the mean of the training samples or zero if `with_mean=False`,\nand `s` is the standard deviation of the training samples or one if\n`with_std=False`.\n\nCentering and scaling happen independently on each feature by computing\nthe relevant statistics on the samples in the training set. Mean and\nstandard deviation are then stored to be used on later data using\n:meth:`transform`.\n\nStandardization of a dataset is a common requirement for many\nmachine learning estimators: they might behave badly if the\nindividual features do not more or less look like standard normally\ndistributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of\na learning algorithm (such as the RBF kernel of Support Vector\nMachines or the L1 and L2 regularizers of linear models) assume that\nall features are centered around 0 a...","upload_date":"2021-08-27T10:56:27","language":"English","dependencies":"sklearn==0.24.2\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"copy","data_type":"bool","default_value":"true","description":"If False, try to avoid a copy and do inplace scaling instead\n This is not guaranteed to always work inplace; e.g. if the data is\n not a NumPy array or scipy.sparse CSR matrix, a copy may still be\n returned"},{"name":"with_mean","data_type":"bool","default_value":"true","description":"If True, center the data before scaling\n This does not work (and will raise an exception) when attempted on\n sparse matrices, because centering them entails building a dense\n matrix which in common use cases is likely to be too large to fit in\n memory"},{"name":"with_std","data_type":"bool","default_value":"true","description":"If True, scale the data to unit variance (or equivalently,\n unit standard deviation)."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.24.2"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.24.2"]}}