{"flow":{"id":"17764","uploader":"12269","name":"sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.svm._classes.SVC)","custom_name":"sklearn.Pipeline(QuantileTransformer,SVC)","class_name":"sklearn.pipeline.Pipeline","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","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":"2020-05-18T23:55:45","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"memory","data_type":"None","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\": \"step_0\", \"step_name\": \"step_0\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"step_1\", \"step_name\": \"step_1\"}}]","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":"step_1","flow":{"id":"17707","uploader":"12269","name":"sklearn.svm._classes.SVC","custom_name":"sklearn.SVC","class_name":"sklearn.svm._classes.SVC","version":"4","external_version":"openml==0.10.2,sklearn==0.22.1","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":"2020-05-18T19:46:42","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"C","data_type":"float","default_value":"4.3827607743865347e-07","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"},{"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)\n\nclass_weight : {dict, 'balanced'}, optional\n 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":"class_weight","data_type":[],"default_value":"null","description":[]},{"name":"coef0","data_type":"float","default_value":"-15.332261879793727","description":"Independent term in kernel function\n It is only significant in 'poly' and 'sigmoid'"},{"name":"decision_function_shape","data_type":"'ovo'","default_value":"\"ovr\"","description":"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\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":"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, optional (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":"0.0010017639673572045","description":[]},{"name":"kernel","data_type":"string","default_value":"\"sigmoid\"","description":"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":"max_iter","data_type":"int","default_value":"-1","description":"Hard limit on iterations within solver, or -1 for no limit"},{"name":"probability","data_type":"boolean","default_value":"true","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":"42","description":"The seed of the pseudo random number generator used when shuffling\n the data for probability estimates. If int, random_state is the\n seed used by the random number generator; If RandomState instance,\n random_state is the random number generator; If None, the random\n number generator is the RandomState instance used by `np.random`."},{"name":"shrinking","data_type":"boolean","default_value":"false","description":"Whether to use the shrinking heuristic"},{"name":"tol","data_type":"float","default_value":"0.00013562683316384962","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_0.22.1"]}},{"identifier":"step_0","flow":{"id":"17755","uploader":"12269","name":"sklearn.preprocessing._data.QuantileTransformer","custom_name":"sklearn.QuantileTransformer","class_name":"sklearn.preprocessing._data.QuantileTransformer","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Transform features using quantiles information.\n\nThis method transforms the features to follow a uniform or a normal\ndistribution. Therefore, for a given feature, this transformation tends\nto spread out the most frequent values. It also reduces the impact of\n(marginal) outliers: this is therefore a robust preprocessing scheme.\n\nThe transformation is applied on each feature independently. First an\nestimate of the cumulative distribution function of a feature is\nused to map the original values to a uniform distribution. The obtained\nvalues are then mapped to the desired output distribution using the\nassociated quantile function. Features values of new\/unseen data that fall\nbelow or above the fitted range will be mapped to the bounds of the output\ndistribution. Note that this transform is non-linear. It may distort linear\ncorrelations between variables measured at the same scale but renders\nvariables measured at different scales more directly comparable.","upload_date":"2020-05-18T23:52:20","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"copy","data_type":"boolean","default_value":"false","description":"Set to False to perform inplace transformation and avoid a copy (if the\n input is already a numpy array)."},{"name":"ignore_implicit_zeros","data_type":"bool","default_value":"false","description":"Only applies to sparse matrices. If True, the sparse entries of the\n matrix are discarded to compute the quantile statistics. If False,\n these entries are treated as zeros"},{"name":"n_quantiles","data_type":"int","default_value":"1200","description":"Number of quantiles to be computed. It corresponds to the number\n of landmarks used to discretize the cumulative distribution function\n If n_quantiles is larger than the number of samples, n_quantiles is set\n to the number of samples as a larger number of quantiles does not give\n a better approximation of the cumulative distribution function\n estimator"},{"name":"output_distribution","data_type":"str","default_value":"\"normal\"","description":"Marginal distribution for the transformed data. The choices are\n 'uniform' (default) or 'normal'"},{"name":"random_state","data_type":"int","default_value":"42","description":"If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by np.random. Note that this is used by subsampling and smoothing\n noise"},{"name":"subsample","data_type":"int","default_value":"34199164","description":"Maximum number of samples used to estimate the quantiles for\n computational efficiency. Note that the subsampling procedure may\n differ for value-identical sparse and dense matrices"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}