{"flow":{"id":"17808","uploader":"12269","name":"sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.tree._classes.DecisionTreeClassifier)","custom_name":"sklearn.Pipeline(FactorAnalysis,DecisionTreeClassifier)","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":"2020-05-19T00:12:24","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":"17504","uploader":"11295","name":"sklearn.tree._classes.DecisionTreeClassifier","custom_name":"sklearn.DecisionTreeClassifier","class_name":"sklearn.tree._classes.DecisionTreeClassifier","version":"3","external_version":"openml==0.10.2,sklearn==0.22.1","description":"A decision tree classifier.","upload_date":"2020-02-08T19:46:35","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","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`` 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 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":"presort","data_type":"deprecated","default_value":"\"deprecated\"","description":"This parameter is deprecated and will be removed in v0.24\n\n .. deprecated:: 0.22"},{"name":"random_state","data_type":"int or RandomState","default_value":"null","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":"splitter","data_type":[],"default_value":"\"best\"","description":[]}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}},{"identifier":"step_0","flow":{"id":"17734","uploader":"12269","name":"sklearn.decomposition._factor_analysis.FactorAnalysis","custom_name":"sklearn.FactorAnalysis","class_name":"sklearn.decomposition._factor_analysis.FactorAnalysis","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Factor Analysis (FA)\n\nA simple linear generative model with Gaussian latent variables.\n\nThe observations are assumed to be caused by a linear transformation of\nlower dimensional latent factors and added Gaussian noise.\nWithout loss of generality the factors are distributed according to a\nGaussian with zero mean and unit covariance. The noise is also zero mean\nand has an arbitrary diagonal covariance matrix.\n\nIf we would restrict the model further, by assuming that the Gaussian\nnoise is even isotropic (all diagonal entries are the same) we would obtain\n:class:`PPCA`.\n\nFactorAnalysis performs a maximum likelihood estimate of the so-called\n`loading` matrix, the transformation of the latent variables to the\nobserved ones, using SVD based approach.","upload_date":"2020-05-18T23:44:12","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"copy","data_type":"bool","default_value":"false","description":"Whether to make a copy of X. If ``False``, the input X gets overwritten\n during fitting"},{"name":"iterated_power","data_type":"int","default_value":"3","description":"Number of iterations for the power method. 3 by default. Only used\n if ``svd_method`` equals 'randomized'"},{"name":"max_iter","data_type":"int","default_value":"7723","description":"Maximum number of iterations"},{"name":"n_components","data_type":"int","default_value":"3","description":"Dimensionality of latent space, the number of components\n of ``X`` that are obtained after ``transform``\n If None, n_components is set to the number of features"},{"name":"noise_variance_init","data_type":"None","default_value":"null","description":"The initial guess of the noise variance for each feature\n If None, it defaults to np.ones(n_features)\n\nsvd_method : {'lapack', 'randomized'}\n Which SVD method to use. If 'lapack' use standard SVD from\n scipy.linalg, if 'randomized' use fast ``randomized_svd`` function\n Defaults to 'randomized'. For most applications 'randomized' will\n be sufficiently precise while providing significant speed gains\n Accuracy can also be improved by setting higher values for\n `iterated_power`. If this is not sufficient, for maximum precision\n you should choose 'lapack'"},{"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`. Only used when ``svd_method`` equals 'randomized'."},{"name":"svd_method","data_type":[],"default_value":"\"lapack\"","description":[]},{"name":"tol","data_type":"float","default_value":"1.841563435236402","description":"Stopping tolerance for log-likelihood increase"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}