{"flow":{"id":"18591","uploader":"8323","name":"sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)","custom_name":"sklearn.Pipeline(SimpleImputer,StandardScaler,LogisticRegression)","class_name":"sklearn.pipeline.Pipeline","version":"1","external_version":"openml==0.10.2,sklearn==0.21.2","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":"2020-07-10T21:14:10","language":"English","dependencies":"sklearn==0.21.2\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\": \"simpleimputer\", \"step_name\": \"simpleimputer\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"standardscaler\", \"step_name\": \"standardscaler\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"logisticregression\", \"step_name\": \"logisticregression\"}}]","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":"boolean","default_value":"false","description":"If True, the time elapsed while fitting each step will be printed as it\n is completed."}],"component":[{"identifier":"standardscaler","flow":{"id":"17405","uploader":"1","name":"sklearn.preprocessing.data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing.data.StandardScaler","version":"35","external_version":"openml==0.10.2,sklearn==0.21.2","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 the\n`transform` method.\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 aroun...","upload_date":"2019-11-22T01:19:36","language":"English","dependencies":"sklearn==0.21.2\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"copy","data_type":"boolean","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":"boolean","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":"boolean","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_0.21.2"]}},{"identifier":"simpleimputer","flow":{"id":"17407","uploader":"1","name":"sklearn.impute._base.SimpleImputer","custom_name":"sklearn.SimpleImputer","class_name":"sklearn.impute._base.SimpleImputer","version":"11","external_version":"openml==0.10.2,sklearn==0.21.2","description":"Imputation transformer for completing missing values.","upload_date":"2019-11-22T01:19:36","language":"English","dependencies":"sklearn==0.21.2\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"add_indicator","data_type":"boolean","default_value":"false","description":"If True, a `MissingIndicator` transform will stack onto output\n of the imputer's transform. This allows a predictive estimator\n to account for missingness despite imputation. If a feature has no\n missing values at fit\/train time, the feature won't appear on\n the missing indicator even if there are missing values at\n transform\/test time."},{"name":"copy","data_type":"boolean","default_value":"true","description":"If True, a copy of X will be created. If False, imputation will\n be done in-place whenever possible. Note that, in the following cases,\n a new copy will always be made, even if `copy=False`:\n\n - If X is not an array of floating values;\n - If X is encoded as a CSR matrix;\n - If add_indicator=True"},{"name":"fill_value","data_type":"string or numerical value","default_value":"-1","description":"When strategy == \"constant\", fill_value is used to replace all\n occurrences of missing_values\n If left to the default, fill_value will be 0 when imputing numerical\n data and \"missing_value\" for strings or object data types"},{"name":"missing_values","data_type":"number","default_value":"NaN","description":"The placeholder for the missing values. All occurrences of\n `missing_values` will be imputed"},{"name":"strategy","data_type":"string","default_value":"\"constant\"","description":"The imputation strategy\n\n - If \"mean\", then replace missing values using the mean along\n each column. Can only be used with numeric data\n - If \"median\", then replace missing values using the median along\n each column. Can only be used with numeric data\n - If \"most_frequent\", then replace missing using the most frequent\n value along each column. Can be used with strings or numeric data\n - If \"constant\", then replace missing values with fill_value. Can be\n used with strings or numeric data\n\n .. versionadded:: 0.20\n strategy=\"constant\" for fixed value imputation"},{"name":"verbose","data_type":"integer","default_value":"0","description":"Controls the verbosity of the imputer"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.21.2"]}},{"identifier":"logisticregression","flow":{"id":"17462","uploader":"10792","name":"sklearn.linear_model.logistic.LogisticRegression","custom_name":"sklearn.LogisticRegression","class_name":"sklearn.linear_model.logistic.LogisticRegression","version":"33","external_version":"openml==0.10.2,sklearn==0.21.2","description":"Logistic Regression (aka logit, MaxEnt) classifier.\n\nIn the multiclass case, the training algorithm uses the one-vs-rest (OvR)\nscheme if the 'multi_class' option is set to 'ovr', and uses the\ncross-entropy loss if the 'multi_class' option is set to 'multinomial'.\n(Currently the 'multinomial' option is supported only by the 'lbfgs',\n'sag', 'saga' and 'newton-cg' solvers.)\n\nThis class implements regularized logistic regression using the\n'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note\nthat regularization is applied by default**. It can handle both dense\nand sparse input. Use C-ordered arrays or CSR matrices containing 64-bit\nfloats for optimal performance; any other input format will be converted\n(and copied).\n\nThe 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization\nwith primal formulation, or no regularization. The 'liblinear' solver\nsupports both L1 and L2 regularization, with a dual formulation only for\nthe L2 penalty. The Elastic-Net regularization is only su...","upload_date":"2019-12-16T00:38:14","language":"English","dependencies":"sklearn==0.21.2\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"C","data_type":"float","default_value":"100000000","description":"Inverse of regularization strength; must be a positive float\n Like in support vector machines, smaller values specify stronger\n regularization"},{"name":"class_weight","data_type":"dict or","default_value":"null","description":"Weights associated with classes in the form ``{class_label: weight}``\n If not given, all classes are supposed to have weight one\n\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))``\n\n Note that these weights will be multiplied with sample_weight (passed\n through the fit method) if sample_weight is specified\n\n .. versionadded:: 0.17\n *class_weight='balanced'*"},{"name":"dual","data_type":"bool","default_value":"false","description":"Dual or primal formulation. Dual formulation is only implemented for\n l2 penalty with liblinear solver. Prefer dual=False when\n n_samples > n_features"},{"name":"fit_intercept","data_type":"bool","default_value":"true","description":"Specifies if a constant (a.k.a. bias or intercept) should be\n added to the decision function"},{"name":"intercept_scaling","data_type":"float","default_value":"1","description":"Useful only when the solver 'liblinear' is used\n and self.fit_intercept is set to True. In this case, x becomes\n [x, self.intercept_scaling],\n i.e. a \"synthetic\" feature with constant value equal to\n intercept_scaling is appended to the instance vector\n The intercept becomes ``intercept_scaling * synthetic_feature_weight``\n\n Note! the synthetic feature weight is subject to l1\/l2 regularization\n as all other features\n To lessen the effect of regularization on synthetic feature weight\n (and therefore on the intercept) intercept_scaling has to be increased"},{"name":"l1_ratio","data_type":"float or None","default_value":"null","description":"The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only\n used if ``penalty='elasticnet'`. Setting ``l1_ratio=0`` is equivalent\n to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent\n to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a\n combination of L1 and L2."},{"name":"max_iter","data_type":"int","default_value":"100","description":"Maximum number of iterations taken for the solvers to converge"},{"name":"multi_class","data_type":"str","default_value":"\"warn\"","description":"If the option chosen is 'ovr', then a binary problem is fit for each\n label. For 'multinomial' the loss minimised is the multinomial loss fit\n across the entire probability distribution, *even when the data is\n binary*. 'multinomial' is unavailable when solver='liblinear'\n 'auto' selects 'ovr' if the data is binary, or if solver='liblinear',\n and otherwise selects 'multinomial'\n\n .. versionadded:: 0.18\n Stochastic Average Gradient descent solver for 'multinomial' case\n .. versionchanged:: 0.20\n Default will change from 'ovr' to 'auto' in 0.22"},{"name":"n_jobs","data_type":"int or None","default_value":"null","description":"Number of CPU cores used when parallelizing over classes if\n multi_class='ovr'\". This parameter is ignored when the ``solver`` is\n set to 'liblinear' regardless of whether 'multi_class' is specified or\n not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n context. ``-1`` means using all processors\n See :term:`Glossary ` for more details"},{"name":"penalty","data_type":"str","default_value":"\"l2\"","description":"Used to specify the norm used in the penalization. The 'newton-cg',\n 'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is\n only supported by the 'saga' solver. If 'none' (not supported by the\n liblinear solver), no regularization is applied\n\n .. versionadded:: 0.19\n l1 penalty with SAGA solver (allowing 'multinomial' + L1)"},{"name":"random_state","data_type":"int","default_value":"22823","description":"The seed of the pseudo random number generator to use when shuffling\n the data. If int, random_state is the seed used by the random number\n generator; If RandomState instance, random_state is the random number\n generator; If None, the random number generator is the RandomState\n instance used by `np.random`. Used when ``solver`` == 'sag' or\n 'liblinear'"},{"name":"solver","data_type":"str","default_value":"\"warn\"","description":"Algorithm to use in the optimization problem\n\n - For small datasets, 'liblinear' is a good choice, whereas 'sag' and\n 'saga' are faster for large ones\n - For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'\n handle multinomial loss; 'liblinear' is limited to one-versus-rest\n schemes\n - 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty\n - 'liblinear' and 'saga' also handle L1 penalty\n - 'saga' also supports 'elasticnet' penalty\n - 'liblinear' does not handle no penalty\n\n Note that 'sag' and 'saga' fast convergence is only guaranteed on\n features with approximately the same scale. You can\n preprocess the data with a scaler from sklearn.preprocessing\n\n .. versionadded:: 0.17\n Stochastic Average Gradient descent solver\n .. versionadded:: 0.19\n SAGA solver\n .. versionchanged:: 0.20\n Default will change from 'liblinear' to 'lbfgs' in 0.22"},{"name":"tol","data_type":"float","default_value":"0.0001","description":"Tolerance for stopping criteria"},{"name":"verbose","data_type":"int","default_value":"0","description":"For the liblinear and lbfgs solvers set verbose to any positive\n number for verbosity"},{"name":"warm_start","data_type":"bool","default_value":"false","description":"When set to True, reuse the solution of the previous call to fit as\n initialization, otherwise, just erase the previous solution\n Useless for liblinear solver. See :term:`the Glossary `\n\n .. versionadded:: 0.17\n *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.21.2"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.21.2"]}}