18453 12269 sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) sklearn.Pipeline(MissingIndicator,LinearDiscriminantAnalysis) sklearn.pipeline.Pipeline 1 openml==0.10.2,sklearn==0.22.1 Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``. 2020-05-21T07:49:53 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 memory None null Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute ``named_steps`` or ``steps`` to inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming steps list [{"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"}}] List of (name, transform) tuples (implementing fit/transform) that are chained, in the order in which they are chained, with the last object an estimator verbose bool false If True, the time elapsed while fitting each step will be printed as it is completed. step_1 17705 12269 sklearn.discriminant_analysis.LinearDiscriminantAnalysis sklearn.LinearDiscriminantAnalysis sklearn.discriminant_analysis.LinearDiscriminantAnalysis 4 openml==0.10.2,sklearn==0.22.1 Linear Discriminant Analysis A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes' rule. The model fits a Gaussian density to each class, assuming that all classes share the same covariance matrix. The fitted model can also be used to reduce the dimensionality of the input by projecting it to the most discriminative directions. .. versionadded:: 0.17 *LinearDiscriminantAnalysis*. 2020-05-18T19:46:37 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 n_components int 346 Number of components (<= min(n_classes - 1, n_features)) for dimensionality reduction. If None, will be set to min(n_classes - 1, n_features) priors array null Class priors shrinkage string or float 0.4904334135320484 Shrinkage parameter, possible values: - None: no shrinkage (default) - 'auto': automatic shrinkage using the Ledoit-Wolf lemma - float between 0 and 1: fixed shrinkage parameter Note that shrinkage works only with 'lsqr' and 'eigen' solvers solver string "eigen" Solver to use, possible values: - 'svd': Singular value decomposition (default) Does not compute the covariance matrix, therefore this solver is recommended for data with a large number of features - 'lsqr': Least squares solution, can be combined with shrinkage - 'eigen': Eigenvalue decomposition, can be combined with shrinkage store_covariance bool false Additionally compute class covariance matrix (default False), used only in 'svd' solver .. versionadded:: 0.17 tol float 0.0012994132677570124 Threshold used for rank estimation in SVD solver .. versionadded:: 0.17 openml-python python scikit-learn sklearn sklearn_0.22.1 step_0 18449 12269 sklearn.impute._base.MissingIndicator sklearn.MissingIndicator sklearn.impute._base.MissingIndicator 1 openml==0.10.2,sklearn==0.22.1 Binary indicators for missing values. Note that this component typically should not be used in a vanilla :class:`Pipeline` consisting of transformers and a classifier, but rather could be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`. 2020-05-21T07:48:00 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 error_on_new boolean true If True (default), transform will raise an error when there are features with missing values in transform that have no missing values in fit. This is applicable only when ``features="missing-only"``. features str "all" Whether the imputer mask should represent all or a subset of features - If "missing-only" (default), the imputer mask will only represent features containing missing values during fit time - If "all", the imputer mask will represent all features missing_values number NaN The placeholder for the missing values. All occurrences of `missing_values` will be indicated (True in the output array), the other values will be marked as False sparse boolean or "auto" Whether the imputer mask format should be sparse or dense - If "auto" (default), the imputer mask will be of same type as input - If True, the imputer mask will be a sparse matrix - If False, the imputer mask will be a numpy array openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1