18575 12269 sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.naive_bayes.MultinomialNB) sklearn.Pipeline(QuantileTransformer,MultinomialNB) 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``. 2020-05-21T19:52:04 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 17700 12269 sklearn.naive_bayes.MultinomialNB sklearn.MultinomialNB sklearn.naive_bayes.MultinomialNB 6 openml==0.10.2,sklearn==0.22.1 Naive Bayes classifier for multinomial models The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial distribution normally requires integer feature counts. However, in practice, fractional counts such as tf-idf may also work. 2020-05-18T19:37:58 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 alpha float 0.8683682482864065 Additive (Laplace/Lidstone) smoothing parameter (0 for no smoothing) class_prior array null Prior probabilities of the classes. If specified the priors are not adjusted according to the data. fit_prior boolean false 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 17755 12269 sklearn.preprocessing._data.QuantileTransformer sklearn.QuantileTransformer sklearn.preprocessing._data.QuantileTransformer 1 openml==0.10.2,sklearn==0.22.1 Transform features using quantiles information. This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme. The transformation is applied on each feature independently. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is non-linear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable. 2020-05-18T23:52:20 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 copy boolean false Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array). ignore_implicit_zeros bool false Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. If False, these entries are treated as zeros n_quantiles int 1200 Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator output_distribution str "normal" Marginal distribution for the transformed data. The choices are 'uniform' (default) or 'normal' random_state int 42 If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. Note that this is used by subsampling and smoothing noise subsample int 34199164 Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1