17982 12269 sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_1=sklearn.naive_bayes.BernoulliNB) sklearn.Pipeline(VarianceThreshold,BernoulliNB) 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``. 2020-05-19T07:21:44 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 17698 12269 sklearn.naive_bayes.BernoulliNB sklearn.BernoulliNB sklearn.naive_bayes.BernoulliNB 11 openml==0.10.2,sklearn==0.22.1 Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB is designed for binary/boolean features. 2020-05-18T19:37:55 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 alpha float 45.72041457701043 Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing) binarize float or None 0.0 Threshold for binarizing (mapping to booleans) of sample features If None, input is presumed to already consist of binary vectors class_prior array null Prior probabilities of the classes. If specified the priors are not adjusted according to the data. fit_prior bool true Whether to learn class prior probabilities or not If false, a uniform prior will be used openml-python python scikit-learn sklearn sklearn_0.22.1 step_0 17744 12269 sklearn.feature_selection._variance_threshold.VarianceThreshold sklearn.VarianceThreshold sklearn.feature_selection._variance_threshold.VarianceThreshold 2 openml==0.10.2,sklearn==0.22.1 Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning. 2020-05-18T23:48:44 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 threshold float 0.0 Features with a training-set variance lower than this threshold will be removed. The default is to keep all features with non-zero variance, i.e. remove the features that have the same value in all samples. openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1