{"flow":{"id":"19545","uploader":"37152","name":"sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)","custom_name":"sklearn.Pipeline(ColumnTransformer,VarianceThreshold,RandomForestClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"2","external_version":"openml==0.14.1,sklearn==1.2.1","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. For this, it\nenables setting parameters of the various steps using their names and the\nparameter name separated by a `'__'`, as in the example below. A step's\nestimator may be replaced entirely by setting the parameter with its name\nto another estimator, or a transformer removed by setting it to\n`'passthrough'` or `None`.","upload_date":"2023-08-07T10:45:09","language":"English","dependencies":"sklearn==1.2.1\nnumpy>=1.17.3\nscipy>=1.3.2\njoblib>=1.1.1\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 of tuple","default_value":"[{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"columntransformer\", \"step_name\": \"columntransformer\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"variancethreshold\", \"step_name\": \"variancethreshold\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"randomforestclassifier\", \"step_name\": \"randomforestclassifier\"}}]","description":"List of (name, transform) tuples (implementing `fit`\/`transform`) that\n are chained in sequential order. The last transform must be an\n 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":"columntransformer","flow":{"id":"19546","uploader":"37152","name":"sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)","custom_name":"sklearn.ColumnTransformer","class_name":"sklearn.compose._column_transformer.ColumnTransformer","version":"2","external_version":"openml==0.14.1,sklearn==1.2.1","description":"Applies transformers to columns of an array or pandas DataFrame.\n\nThis estimator allows different columns or column subsets of the input\nto be transformed separately and the features generated by each transformer\nwill be concatenated to form a single feature space.\nThis is useful for heterogeneous or columnar data, to combine several\nfeature extraction mechanisms or transformations into a single transformer.","upload_date":"2023-08-07T10:45:09","language":"English","dependencies":"sklearn==1.2.1\nnumpy>=1.17.3\nscipy>=1.3.2\njoblib>=1.1.1\nthreadpoolctl>=2.0.0","parameter":[{"name":"n_jobs","data_type":"int","default_value":"null","description":"Number of jobs to run in parallel\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":"remainder","data_type":[],"default_value":"\"passthrough\"","description":[]},{"name":"sparse_threshold","data_type":"float","default_value":"0.3","description":"If the output of the different transformers contains sparse matrices,\n these will be stacked as a sparse matrix if the overall density is\n lower than this value. Use ``sparse_threshold=0`` to always return\n dense. When the transformed output consists of all dense data, the\n stacked result will be dense, and this keyword will be ignored"},{"name":"transformer_weights","data_type":"dict","default_value":"null","description":"Multiplicative weights for features per transformer. The output of the\n transformer is multiplied by these weights. Keys are transformer names,\n values the weights"},{"name":"transformers","data_type":"list of tuples","default_value":"[{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"numeric\", \"step_name\": \"numeric\", \"argument_1\": [1, 2]}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"nominal\", \"step_name\": \"nominal\", \"argument_1\": [0, 3]}}]","description":"List of (name, transformer, columns) tuples specifying the\n transformer objects to be applied to subsets of the data"},{"name":"verbose","data_type":"bool","default_value":"false","description":"If True, the time elapsed while fitting each transformer will be\n printed as it is completed"},{"name":"verbose_feature_names_out","data_type":"bool","default_value":"true","description":"If True, :meth:`get_feature_names_out` will prefix all feature names\n with the name of the transformer that generated that feature\n If False, :meth:`get_feature_names_out` will not prefix any feature\n names and will error if feature names are not unique\n\n .. versionadded:: 1.0"}],"component":[{"identifier":"numeric","flow":{"id":"19547","uploader":"37152","name":"sklearn.preprocessing._data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing._data.StandardScaler","version":"18","external_version":"openml==0.14.1,sklearn==1.2.1","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 ...","upload_date":"2023-08-07T10:45:09","language":"English","dependencies":"sklearn==1.2.1\nnumpy>=1.17.3\nscipy>=1.3.2\njoblib>=1.1.1\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_1.2.1"]}},{"identifier":"nominal","flow":{"id":"19548","uploader":"37152","name":"sklearn.preprocessing._encoders.OneHotEncoder","custom_name":"sklearn.OneHotEncoder","class_name":"sklearn.preprocessing._encoders.OneHotEncoder","version":"49","external_version":"openml==0.14.1,sklearn==1.2.1","description":"Encode categorical features as a one-hot numeric array.\n\nThe input to this transformer should be an array-like of integers or\nstrings, denoting the values taken on by categorical (discrete) features.\nThe features are encoded using a one-hot (aka 'one-of-K' or 'dummy')\nencoding scheme. This creates a binary column for each category and\nreturns a sparse matrix or dense array (depending on the ``sparse_output``\nparameter)\n\nBy default, the encoder derives the categories based on the unique values\nin each feature. Alternatively, you can also specify the `categories`\nmanually.\n\nThis encoding is needed for feeding categorical data to many scikit-learn\nestimators, notably linear models and SVMs with the standard kernels.\n\nNote: a one-hot encoding of y labels should use a LabelBinarizer\ninstead.","upload_date":"2023-08-07T10:45:09","language":"English","dependencies":"sklearn==1.2.1\nnumpy>=1.17.3\nscipy>=1.3.2\njoblib>=1.1.1\nthreadpoolctl>=2.0.0","parameter":[{"name":"categories","data_type":"'auto' or a list of array","default_value":"\"auto\"","description":"Categories (unique values) per feature:\n\n - 'auto' : Determine categories automatically from the training data\n - list : ``categories[i]`` holds the categories expected in the ith\n column. The passed categories should not mix strings and numeric\n values within a single feature, and should be sorted in case of\n numeric values\n\n The used categories can be found in the ``categories_`` attribute\n\n .. versionadded:: 0.20\n\ndrop : {'first', 'if_binary'} or an array-like of shape (n_features,), default=None\n Specifies a methodology to use to drop one of the categories per\n feature. This is useful in situations where perfectly collinear\n features cause problems, such as when feeding the resulting data\n into an unregularized linear regression model\n\n However, dropping one category breaks the symmetry of the original\n representation and can therefore induce a bias in downstream models,\n for instance for penalized linear classification or regression models"},{"name":"drop","data_type":[],"default_value":"null","description":[]},{"name":"dtype","data_type":"number type","default_value":"{\"oml-python:serialized_object\": \"type\", \"value\": \"np.float64\"}","description":"Desired dtype of output\n\nhandle_unknown : {'error', 'ignore', 'infrequent_if_exist'}, default='error'\n Specifies the way unknown categories are handled during :meth:`transform`\n\n - 'error' : Raise an error if an unknown category is present during transform\n - 'ignore' : When an unknown category is encountered during\n transform, the resulting one-hot encoded columns for this feature\n will be all zeros. In the inverse transform, an unknown category\n will be denoted as None\n - 'infrequent_if_exist' : When an unknown category is encountered\n during transform, the resulting one-hot encoded columns for this\n feature will map to the infrequent category if it exists. The\n infrequent category will be mapped to the last position in the\n encoding. During inverse transform, an unknown category will be\n mapped to the category denoted `'infrequent'` if it exists. If the\n `'infrequent'` category does not exist, then :meth:`transform` and\n ..."},{"name":"handle_unknown","data_type":[],"default_value":"\"ignore\"","description":[]},{"name":"max_categories","data_type":"int","default_value":"null","description":"Specifies an upper limit to the number of output features for each input\n feature when considering infrequent categories. If there are infrequent\n categories, `max_categories` includes the category representing the\n infrequent categories along with the frequent categories. If `None`,\n there is no limit to the number of output features\n\n .. versionadded:: 1.1\n Read more in the :ref:`User Guide `."},{"name":"min_frequency","data_type":"int or float","default_value":"null","description":"Specifies the minimum frequency below which a category will be\n considered infrequent\n\n - If `int`, categories with a smaller cardinality will be considered\n infrequent\n\n - If `float`, categories with a smaller cardinality than\n `min_frequency * n_samples` will be considered infrequent\n\n .. versionadded:: 1.1\n Read more in the :ref:`User Guide `"},{"name":"sparse","data_type":"bool","default_value":"\"deprecated\"","description":"Will return sparse matrix if set True else will return an array\n\n .. deprecated:: 1.2\n `sparse` is deprecated in 1.2 and will be removed in 1.4. Use\n `sparse_output` instead"},{"name":"sparse_output","data_type":"bool","default_value":"true","description":"Will return sparse matrix if set True else will return an array\n\n .. versionadded:: 1.2\n `sparse` was renamed to `sparse_output`"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.2.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.2.1"]}},{"identifier":"variancethreshold","flow":{"id":"19549","uploader":"37152","name":"sklearn.feature_selection._variance_threshold.VarianceThreshold","custom_name":"sklearn.VarianceThreshold","class_name":"sklearn.feature_selection._variance_threshold.VarianceThreshold","version":"13","external_version":"openml==0.14.1,sklearn==1.2.1","description":"Feature selector that removes all low-variance features.\n\nThis feature selection algorithm looks only at the features (X), not the\ndesired outputs (y), and can thus be used for unsupervised learning.","upload_date":"2023-08-07T10:45:09","language":"English","dependencies":"sklearn==1.2.1\nnumpy>=1.17.3\nscipy>=1.3.2\njoblib>=1.1.1\nthreadpoolctl>=2.0.0","parameter":{"name":"threshold","data_type":"float","default_value":"0.0","description":"Features with a training-set variance lower than this threshold will\n be removed. The default is to keep all features with non-zero variance,\n i.e. remove the features that have the same value in all samples."},"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.2.1"]}},{"identifier":"randomforestclassifier","flow":{"id":"19550","uploader":"37152","name":"sklearn.ensemble._forest.RandomForestClassifier","custom_name":"sklearn.RandomForestClassifier","class_name":"sklearn.ensemble._forest.RandomForestClassifier","version":"33","external_version":"openml==0.14.1,sklearn==1.2.1","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 controlled with the `max_samples` parameter if\n`bootstrap=True` (default), otherwise the whole dataset is used to build\neach tree.","upload_date":"2023-08-07T10:45:09","language":"English","dependencies":"sklearn==1.2.1\nnumpy>=1.17.3\nscipy>=1.3.2\njoblib>=1.1.1\nthreadpoolctl>=2.0.0","parameter":[{"name":"bootstrap","data_type":"bool","default_value":"true","description":"Whether bootstrap samples are used when building trees. If False, the\n whole dataset is used to build each tree"},{"name":"ccp_alpha","data_type":"non","default_value":"0.0","description":"Complexity parameter used for Minimal Cost-Complexity Pruning. The\n subtree with the largest cost complexity that is smaller than\n ``ccp_alpha`` will be chosen. By default, no pruning is performed. See\n :ref:`minimal_cost_complexity_pruning` for details\n\n .. versionadded:: 0.22"},{"name":"class_weight","data_type":[],"default_value":"null","description":[]},{"name":"criterion","data_type":[],"default_value":"\"gini\"","description":[]},{"name":"max_depth","data_type":"int","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":[],"default_value":"\"sqrt\"","description":[]},{"name":"max_leaf_nodes","data_type":"int","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":"max_samples","data_type":"int or float","default_value":"null","description":"If bootstrap is True, the number of samples to draw from X\n to train each base estimator\n\n - If None (default), then draw `X.shape[0]` samples\n - If int, then draw `max_samples` samples\n - If float, then draw `max_samples * X.shape[0]` samples. Thus,\n `max_samples` should be in the interval `(0.0, 1.0]`\n\n .. versionadded:: 0.22"},{"name":"min_impurity_decrease","data_type":"float","default_value":"0.0","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_samples_leaf","data_type":"int or float","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 or float","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\n\nmax_features : {\"sqrt\", \"log2\", None}, int or float, default=\"sqrt\"\n 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 `max(1, int(max_features * n_features_in_))` 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)`\n - If \"log2\", then `max_features=log2(n_features)`\n - If None, then `max_features=n_features`\n\n .. versionchanged:: 1.1\n The default of `max_features` changed from `\"auto\"` to `\"sqrt\"`\n\n .. deprecated:: 1.1\n The `\"auto\"` option was deprecated in 1.1 and will be removed\n in 1.3\n\n Note: the search for a split does not stop until at lea..."},{"name":"n_estimators","data_type":"int","default_value":"100","description":"The number of trees in the forest\n\n .. versionchanged:: 0.22\n The default value of ``n_estimators`` changed from 10 to 100\n in 0.22\n\ncriterion : {\"gini\", \"entropy\", \"log_loss\"}, default=\"gini\"\n The function to measure the quality of a split. Supported criteria are\n \"gini\" for the Gini impurity and \"log_loss\" and \"entropy\" both for the\n Shannon information gain, see :ref:`tree_mathematical_formulation`\n Note: This parameter is tree-specific"},{"name":"n_jobs","data_type":"int","default_value":"null","description":"The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,\n :meth:`decision_path` and :meth:`apply` are all parallelized over the\n trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`\n context. ``-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 the generalization score\n Only available if bootstrap=True"},{"name":"random_state","data_type":"int","default_value":"null","description":"Controls both the randomness of the bootstrapping of the samples used\n when building trees (if ``bootstrap=True``) and the sampling of the\n features to consider when looking for the best split at each node\n (if ``max_features < n_features``)\n See :term:`Glossary ` for details"},{"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:`Glossary ` and\n :ref:`gradient_boosting_warm_start` for details\n\nclass_weight : {\"balanced\", \"balanced_subsample\"}, dict or list of dicts, default=None\n 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 freq..."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.2.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.2.1"]}}