{"flow":{"id":"18446","uploader":"8323","name":"sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)","custom_name":"sklearn.Pipeline(SimpleImputer,StandardScaler,RandomForestClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"3","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":"2020-05-20T23:23:54","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\": \"randomforestclassifier\", \"step_name\": \"randomforestclassifier\"}}]","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":"randomforestclassifier","flow":{"id":"17692","uploader":"8323","name":"sklearn.ensemble.forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble.forest.RandomForestClassifier","version":"64","external_version":"openml==0.10.2,sklearn==0.21.2","description":"A random forest classifier.\n\nA random forest is a meta estimator that fits a number of decision tree\nclassifiers on various sub-samples of the dataset and uses averaging to\nimprove the predictive accuracy and control over-fitting.\nThe sub-sample size is always the same as the original\ninput sample size but the samples are drawn with replacement if\n`bootstrap=True` (default).","upload_date":"2020-05-17T14:27:10","language":"English","dependencies":"sklearn==0.21.2\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"bootstrap","data_type":"boolean","default_value":"true","description":"Whether bootstrap samples are used when building trees. If False, the\n whole datset is used to build each tree"},{"name":"class_weight","data_type":"dict","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. For\n multi-output problems, a list of dicts can be provided in the same\n order as the columns of y\n\n Note that for multioutput (including multilabel) weights should be\n defined for each class of every column in its own dict. For example,\n for four-class multilabel classification weights should be\n [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of\n [{1:1}, {2:5}, {3:1}, {4:1}]\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 The \"balanced_subsample\" mode is the same as \"balanced\" except that\n weights are computed based on the bootstrap sample for every tree\n grown\n\n For multi-output, the weights of each column of y will be multiplied\n\n Note that these weights will be multiplied..."},{"name":"criterion","data_type":"string","default_value":"\"gini\"","description":"The function to measure the quality of a split. Supported criteria are\n \"gini\" for the Gini impurity and \"entropy\" for the information gain\n Note: this parameter is tree-specific"},{"name":"max_depth","data_type":"integer or None","default_value":"null","description":"The maximum depth of the tree. If None, then nodes are expanded until\n all leaves are pure or until all leaves contain less than\n min_samples_split samples"},{"name":"max_features","data_type":"int","default_value":"\"auto\"","description":"The number of features to consider when looking for the best split:\n\n - If int, then consider `max_features` features at each split\n - If float, then `max_features` is a fraction and\n `int(max_features * n_features)` features are considered at each\n split\n - If \"auto\", then `max_features=sqrt(n_features)`\n - If \"sqrt\", then `max_features=sqrt(n_features)` (same as \"auto\")\n - If \"log2\", then `max_features=log2(n_features)`\n - If None, then `max_features=n_features`\n\n Note: the search for a split does not stop until at least one\n valid partition of the node samples is found, even if it requires to\n effectively inspect more than ``max_features`` features"},{"name":"max_leaf_nodes","data_type":"int or None","default_value":"null","description":"Grow trees with ``max_leaf_nodes`` in best-first fashion\n Best nodes are defined as relative reduction in impurity\n If None then unlimited number of leaf nodes"},{"name":"min_impurity_decrease","data_type":"float","default_value":"0","description":"A node will be split if this split induces a decrease of the impurity\n greater than or equal to this value\n\n The weighted impurity decrease equation is the following::\n\n N_t \/ N * (impurity - N_t_R \/ N_t * right_impurity\n - N_t_L \/ N_t * left_impurity)\n\n where ``N`` is the total number of samples, ``N_t`` is the number of\n samples at the current node, ``N_t_L`` is the number of samples in the\n left child, and ``N_t_R`` is the number of samples in the right child\n\n ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,\n if ``sample_weight`` is passed\n\n .. versionadded:: 0.19"},{"name":"min_impurity_split","data_type":"float","default_value":"null","description":"Threshold for early stopping in tree growth. A node will split\n if its impurity is above the threshold, otherwise it is a leaf\n\n .. deprecated:: 0.19\n ``min_impurity_split`` has been deprecated in favor of\n ``min_impurity_decrease`` in 0.19. The default value of\n ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it\n will be removed in 0.25. Use ``min_impurity_decrease`` instead"},{"name":"min_samples_leaf","data_type":"int","default_value":"1","description":"The minimum number of samples required to be at a leaf node\n A split point at any depth will only be considered if it leaves at\n least ``min_samples_leaf`` training samples in each of the left and\n right branches. This may have the effect of smoothing the model,\n especially in regression\n\n - If int, then consider `min_samples_leaf` as the minimum number\n - If float, then `min_samples_leaf` is a fraction and\n `ceil(min_samples_leaf * n_samples)` are the minimum\n number of samples for each node\n\n .. versionchanged:: 0.18\n Added float values for fractions"},{"name":"min_samples_split","data_type":"int","default_value":"2","description":"The minimum number of samples required to split an internal node:\n\n - If int, then consider `min_samples_split` as the minimum number\n - If float, then `min_samples_split` is a fraction and\n `ceil(min_samples_split * n_samples)` are the minimum\n number of samples for each split\n\n .. versionchanged:: 0.18\n Added float values for fractions"},{"name":"min_weight_fraction_leaf","data_type":"float","default_value":"0.0","description":"The minimum weighted fraction of the sum total of weights (of all\n the input samples) required to be at a leaf node. Samples have\n equal weight when sample_weight is not provided"},{"name":"n_estimators","data_type":"integer","default_value":"10","description":"The number of trees in the forest\n\n .. versionchanged:: 0.20\n The default value of ``n_estimators`` will change from 10 in\n version 0.20 to 100 in version 0.22"},{"name":"n_jobs","data_type":"int or None","default_value":"1","description":"The number of jobs to run in parallel for both `fit` and `predict`\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context\n ``-1`` means using all processors. See :term:`Glossary `\n for more details"},{"name":"oob_score","data_type":"bool","default_value":"false","description":"Whether to use out-of-bag samples to estimate\n the generalization accuracy"},{"name":"random_state","data_type":"int","default_value":"1","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`"},{"name":"verbose","data_type":"int","default_value":"0","description":"Controls the verbosity when fitting and predicting"},{"name":"warm_start","data_type":"bool","default_value":"false","description":"When set to ``True``, reuse the solution of the previous call to fit\n and add more estimators to the ensemble, otherwise, just fit a whole\n new forest. See :term:`the Glossary `"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.21.2"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.21.2"]}}