{"flow":{"id":"33773","uploader":"1159","name":"TEST0e83214a4dsklearn.pipeline.Pipeline(ohe=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessing._data.StandardScaler,fu=sklearn.pipeline.FeatureUnion(pca=sklearn.decomposition._truncated_svd.TruncatedSVD,fs=sklearn.feature_selection._univariate_selection.SelectPercentile),boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier))","custom_name":"sklearn.Pipeline(OneHotEncoder,StandardScaler,FeatureUnion,AdaBoostClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","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":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\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\": \"ohe\", \"step_name\": \"ohe\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"scaler\", \"step_name\": \"scaler\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"fu\", \"step_name\": \"fu\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"boosting\", \"step_name\": \"boosting\"}}]","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":"ohe","flow":{"id":"33774","uploader":"1159","name":"TEST0e83214a4dsklearn.preprocessing._encoders.OneHotEncoder","custom_name":"sklearn.OneHotEncoder","class_name":"sklearn.preprocessing._encoders.OneHotEncoder","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","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``\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":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\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 a 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 a neural network or an unregularized regression\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'}, default='error'\n Whether to raise an error or ignore if an unknown categorical feature\n is present during transform (default is to raise). When this parameter\n is set to 'ignore' and 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."},{"name":"handle_unknown","data_type":[],"default_value":"\"ignore\"","description":[]},{"name":"sparse","data_type":"bool","default_value":"true","description":"Will return sparse matrix if set True else will return an array"}]}},{"identifier":"scaler","flow":{"id":"33775","uploader":"1159","name":"TEST0e83214a4dsklearn.preprocessing._data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing._data.StandardScaler","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","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":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\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":"false","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)."}]}},{"identifier":"fu","flow":{"id":"33776","uploader":"1159","name":"TEST0e83214a4dsklearn.pipeline.FeatureUnion(pca=sklearn.decomposition._truncated_svd.TruncatedSVD,fs=sklearn.feature_selection._univariate_selection.SelectPercentile)","custom_name":"sklearn.FeatureUnion(TruncatedSVD,SelectPercentile)","class_name":"sklearn.pipeline.FeatureUnion","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","description":"Concatenates results of multiple transformer objects.\n\nThis estimator applies a list of transformer objects in parallel to the\ninput data, then concatenates the results. This is useful to combine\nseveral feature extraction mechanisms into a single transformer.\n\nParameters of the transformers may be set using its name and the parameter\nname separated by a '__'. A transformer may be replaced entirely by\nsetting the parameter with its name to another transformer,\nor removed by setting to 'drop'.","upload_date":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\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\n\n .. versionchanged:: v0.20\n `n_jobs` default changed from 1 to None"},{"name":"transformer_list","data_type":"list of","default_value":"[{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"pca\", \"step_name\": \"pca\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"fs\", \"step_name\": \"fs\"}}]","description":"List of transformer objects to be applied to the data. The first\n half of each tuple is the name of the transformer. The tranformer can\n be 'drop' for it to be ignored\n\n .. versionchanged:: 0.22\n Deprecated `None` as a transformer in favor of 'drop'"},{"name":"transformer_weights","data_type":"dict","default_value":"null","description":"Multiplicative weights for features per transformer\n Keys are transformer names, values the weights\n Raises ValueError if key not present in ``transformer_list``"},{"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\n\nSee Also\n--------"}],"component":[{"identifier":"pca","flow":{"id":"33777","uploader":"1159","name":"TEST0e83214a4dsklearn.decomposition._truncated_svd.TruncatedSVD","custom_name":"sklearn.TruncatedSVD","class_name":"sklearn.decomposition._truncated_svd.TruncatedSVD","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","description":"Dimensionality reduction using truncated SVD (aka LSA).\n\nThis transformer performs linear dimensionality reduction by means of\ntruncated singular value decomposition (SVD). Contrary to PCA, this\nestimator does not center the data before computing the singular value\ndecomposition. This means it can work with sparse matrices\nefficiently.\n\nIn particular, truncated SVD works on term count\/tf-idf matrices as\nreturned by the vectorizers in :mod:`sklearn.feature_extraction.text`. In\nthat context, it is known as latent semantic analysis (LSA).\n\nThis estimator supports two algorithms: a fast randomized SVD solver, and\na \"naive\" algorithm that uses ARPACK as an eigensolver on `X * X.T` or\n`X.T * X`, whichever is more efficient.","upload_date":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"algorithm","data_type":[],"default_value":"\"randomized\"","description":[]},{"name":"n_components","data_type":"int","default_value":"2","description":"Desired dimensionality of output data\n Must be strictly less than the number of features\n The default value is useful for visualisation. For LSA, a value of\n 100 is recommended\n\nalgorithm : {'arpack', 'randomized'}, default='randomized'\n SVD solver to use. Either \"arpack\" for the ARPACK wrapper in SciPy\n (scipy.sparse.linalg.svds), or \"randomized\" for the randomized\n algorithm due to Halko (2009)"},{"name":"n_iter","data_type":"int","default_value":"5","description":"Number of iterations for randomized SVD solver. Not used by ARPACK. The\n default is larger than the default in\n :func:`~sklearn.utils.extmath.randomized_svd` to handle sparse\n matrices that may have large slowly decaying spectrum"},{"name":"random_state","data_type":"int","default_value":"null","description":"Used during randomized svd. Pass an int for reproducible results across\n multiple function calls\n See :term:`Glossary `"},{"name":"tol","data_type":"float","default_value":"0.0","description":"Tolerance for ARPACK. 0 means machine precision. Ignored by randomized\n SVD solver."}]}},{"identifier":"fs","flow":{"id":"33778","uploader":"1159","name":"TEST0e83214a4dsklearn.feature_selection._univariate_selection.SelectPercentile","custom_name":"sklearn.SelectPercentile","class_name":"sklearn.feature_selection._univariate_selection.SelectPercentile","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","description":"Select features according to a percentile of the highest scores.","upload_date":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"percentile","data_type":"int","default_value":"30","description":"Percent of features to keep."},{"name":"score_func","data_type":"callable","default_value":"{\"oml-python:serialized_object\": \"function\", \"value\": \"sklearn.feature_selection._univariate_selection.f_classif\"}","description":"Function taking two arrays X and y, and returning a pair of arrays\n (scores, pvalues) or a single array with scores\n Default is f_classif (see below \"See Also\"). The default function only\n works with classification tasks\n\n .. versionadded:: 0.18"}]}}]}},{"identifier":"boosting","flow":{"id":"33779","uploader":"1159","name":"TEST0e83214a4dsklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)","custom_name":"sklearn.AdaBoostClassifier","class_name":"sklearn.ensemble._weight_boosting.AdaBoostClassifier","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","description":"An AdaBoost classifier.\n\nAn AdaBoost [1] classifier is a meta-estimator that begins by fitting a\nclassifier on the original dataset and then fits additional copies of the\nclassifier on the same dataset but where the weights of incorrectly\nclassified instances are adjusted such that subsequent classifiers focus\nmore on difficult cases.\n\nThis class implements the algorithm known as AdaBoost-SAMME [2].","upload_date":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"algorithm","data_type":[],"default_value":"\"SAMME.R\"","description":[]},{"name":"base_estimator","data_type":"object","default_value":"{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"base_estimator\", \"step_name\": null}}","description":"The base estimator from which the boosted ensemble is built\n Support for sample weighting is required, as well as proper\n ``classes_`` and ``n_classes_`` attributes. If ``None``, then\n the base estimator is :class:`~sklearn.tree.DecisionTreeClassifier`\n initialized with `max_depth=1`"},{"name":"learning_rate","data_type":"float","default_value":"1.0","description":"Learning rate shrinks the contribution of each classifier by\n ``learning_rate``. There is a trade-off between ``learning_rate`` and\n ``n_estimators``\n\nalgorithm : {'SAMME', 'SAMME.R'}, default='SAMME.R'\n If 'SAMME.R' then use the SAMME.R real boosting algorithm\n ``base_estimator`` must support calculation of class probabilities\n If 'SAMME' then use the SAMME discrete boosting algorithm\n The SAMME.R algorithm typically converges faster than SAMME,\n achieving a lower test error with fewer boosting iterations"},{"name":"n_estimators","data_type":"int","default_value":"50","description":"The maximum number of estimators at which boosting is terminated\n In case of perfect fit, the learning procedure is stopped early"},{"name":"random_state","data_type":"int","default_value":"null","description":"Controls the random seed given at each `base_estimator` at each\n boosting iteration\n Thus, it is only used when `base_estimator` exposes a `random_state`\n Pass an int for reproducible output across multiple function calls\n See :term:`Glossary `."}],"component":{"identifier":"base_estimator","flow":{"id":"33780","uploader":"1159","name":"TEST0e83214a4dsklearn.tree._classes.DecisionTreeClassifier","custom_name":"sklearn.DecisionTreeClassifier","class_name":"sklearn.tree._classes.DecisionTreeClassifier","version":"1","external_version":"openml==0.14.2,sklearn==0.24.0","description":"A decision tree classifier.","upload_date":"2024-01-18T12:00:47","language":"English","dependencies":"sklearn==0.24.0\nnumpy>=1.13.3\nscipy>=0.19.1\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"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":"dict","default_value":"null","description":"Weights associated with classes in the form ``{class_label: weight}``\n If None, 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 For multi-output, the weights of each column of y will be multiplied\n\n Note that these weights will be multiplied with sample_weight (passed\n through the fit method) if sample_weight is specified"},{"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":"int","default_value":"null","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)`\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","default_value":"null","description":"Grow a tree 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":"0.0","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`` has changed from 1e-7 to 0 in 0.23 and it\n will be removed in 1.0 (renaming of 0.25)\n Use ``min_impurity_decrease`` instead"},{"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"},{"name":"random_state","data_type":"int","default_value":"null","description":"Controls the randomness of the estimator. The features are always\n randomly permuted at each split, even if ``splitter`` is set to\n ``\"best\"``. When ``max_features < n_features``, the algorithm will\n select ``max_features`` at random at each split before finding the best\n split among them. But the best found split may vary across different\n runs, even if ``max_features=n_features``. That is the case, if the\n improvement of the criterion is identical for several splits and one\n split has to be selected at random. To obtain a deterministic behaviour\n during fitting, ``random_state`` has to be fixed to an integer\n See :term:`Glossary ` for details"},{"name":"splitter","data_type":[],"default_value":"\"best\"","description":[]}]}}}}]}}