{"flow":{"id":"17762","uploader":"12269","name":"sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)","custom_name":"sklearn.Pipeline(TruncatedSVD,LinearDiscriminantAnalysis)","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":"2020-05-18T23:53:33","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":"17705","uploader":"12269","name":"sklearn.discriminant_analysis.LinearDiscriminantAnalysis","custom_name":"sklearn.LinearDiscriminantAnalysis","class_name":"sklearn.discriminant_analysis.LinearDiscriminantAnalysis","version":"4","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Linear Discriminant Analysis\n\nA classifier with a linear decision boundary, generated by fitting class\nconditional densities to the data and using Bayes' rule.\n\nThe model fits a Gaussian density to each class, assuming that all classes\nshare the same covariance matrix.\n\nThe fitted model can also be used to reduce the dimensionality of the input\nby projecting it to the most discriminative directions.\n\n.. versionadded:: 0.17\n *LinearDiscriminantAnalysis*.","upload_date":"2020-05-18T19:46:37","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"n_components","data_type":"int","default_value":"346","description":"Number of components (<= min(n_classes - 1, n_features)) for\n dimensionality reduction. If None, will be set to\n min(n_classes - 1, n_features)"},{"name":"priors","data_type":"array","default_value":"null","description":"Class priors"},{"name":"shrinkage","data_type":"string or float","default_value":"0.4904334135320484","description":"Shrinkage parameter, possible values:\n - None: no shrinkage (default)\n - 'auto': automatic shrinkage using the Ledoit-Wolf lemma\n - float between 0 and 1: fixed shrinkage parameter\n\n Note that shrinkage works only with 'lsqr' and 'eigen' solvers"},{"name":"solver","data_type":"string","default_value":"\"eigen\"","description":"Solver to use, possible values:\n - 'svd': Singular value decomposition (default)\n Does not compute the covariance matrix, therefore this solver is\n recommended for data with a large number of features\n - 'lsqr': Least squares solution, can be combined with shrinkage\n - 'eigen': Eigenvalue decomposition, can be combined with shrinkage"},{"name":"store_covariance","data_type":"bool","default_value":"false","description":"Additionally compute class covariance matrix (default False), used\n only in 'svd' solver\n\n .. versionadded:: 0.17"},{"name":"tol","data_type":"float","default_value":"0.0012994132677570124","description":"Threshold used for rank estimation in SVD solver\n\n .. versionadded:: 0.17"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}},{"identifier":"step_0","flow":{"id":"17732","uploader":"12269","name":"sklearn.decomposition._truncated_svd.TruncatedSVD","custom_name":"sklearn.TruncatedSVD","class_name":"sklearn.decomposition._truncated_svd.TruncatedSVD","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","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 scipy.sparse matrices\nefficiently.\n\nIn particular, truncated SVD works on term count\/tf-idf matrices as\nreturned by the vectorizers in sklearn.feature_extraction.text. In that\ncontext, 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":"2020-05-18T23:44:04","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"algorithm","data_type":"string","default_value":"\"randomized\"","description":"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_components","data_type":"int","default_value":"1","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"},{"name":"n_iter","data_type":"int","default_value":"95","description":"Number of iterations for randomized SVD solver. Not used by ARPACK. The\n default is larger than the default in\n `~sklearn.utils.extmath.randomized_svd` to handle sparse matrices that\n may have large slowly decaying spectrum"},{"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`"},{"name":"tol","data_type":"float","default_value":"0.0","description":"Tolerance for ARPACK. 0 means machine precision. Ignored by randomized\n SVD solver."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}