17789 12269 sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) sklearn.Pipeline(QuantileTransformer,SGDClassifier) 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``. 2020-05-19T00:03:47 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 17703 12269 sklearn.linear_model._stochastic_gradient.SGDClassifier sklearn.SGDClassifier sklearn.linear_model._stochastic_gradient.SGDClassifier 2 openml==0.10.2,sklearn==0.22.1 Linear classifiers (SVM, logistic regression, a.o.) with SGD training. This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is estimated each sample at a time and the model is updated along the way with a decreasing strength schedule (aka learning rate). SGD allows minibatch (online/out-of-core) learning, see the partial_fit method. For best results using the default learning rate schedule, the data should have zero mean and unit variance. This implementation works with data represented as dense or sparse arrays of floating point values for the features. The model it fits can be controlled with the loss parameter; by default, it fits a linear support vector machine (SVM). The regularizer is a penalty added to the loss function that shrinks model parameters towards the zero vector using either the squared euclidean norm L2 or the absolute norm L1 or a combination of both (Elastic Net). If the parameter update crosses the 0.0 value b... 2020-05-18T19:46:26 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 alpha float 18.576489600940455 Constant that multiplies the regularization term. Defaults to 0.0001 Also used to compute learning_rate when set to 'optimal' average bool or int false When set to True, computes the averaged SGD weights and stores the result in the ``coef_`` attribute. If set to an int greater than 1, averaging will begin once the total number of samples seen reaches average. So ``average=10`` will begin averaging after seeing 10 samples. class_weight dict null Preset for the class_weight fit parameter Weights associated with classes. If not given, all classes are supposed to have weight one The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))`` early_stopping bool true Whether to use early stopping to terminate training when validation score is not improving. If set to True, it will automatically set aside a stratified fraction of training data as validation and terminate training when validation score is not improving by at least tol for n_iter_no_change consecutive epochs .. versionadded:: 0.20 epsilon float 0.9292126060861614 Epsilon in the epsilon-insensitive loss functions; only if `loss` is 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive' For 'huber', determines the threshold at which it becomes less important to get the prediction exactly right For epsilon-insensitive, any differences between the current prediction and the correct label are ignored if they are less than this threshold eta0 double 0.8230008156002588 The initial learning rate for the 'constant', 'invscaling' or 'adaptive' schedules. The default value is 0.0 as eta0 is not used by the default schedule 'optimal' fit_intercept bool false Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True l1_ratio float 0.3888555696189441 The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1 l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1 Defaults to 0.15 learning_rate str "constant" The learning rate schedule: 'constant': eta = eta0 'optimal': [default] eta = 1.0 / (alpha * (t + t0)) where t0 is chosen by a heuristic proposed by Leon Bottou 'invscaling': eta = eta0 / pow(t, power_t) 'adaptive': eta = eta0, as long as the training keeps decreasing Each time n_iter_no_change consecutive epochs fail to decrease the training loss by tol or fail to increase validation score by tol if early_stopping is True, the current learning rate is divided by 5 loss str "modified_huber" The loss function to be used. Defaults to 'hinge', which gives a linear SVM The possible options are 'hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron', or a regression loss: 'squared_loss', 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive' The 'log' loss gives logistic regression, a probabilistic classifier 'modified_huber' is another smooth loss that brings tolerance to outliers as well as probability estimates 'squared_hinge' is like hinge but is quadratically penalized 'perceptron' is the linear loss used by the perceptron algorithm The other losses are designed for regression but can be useful in classification as well; see SGDRegressor for a description penalty : {'l2', 'l1', 'elasticnet'}, default='l2' The penalty (aka regularization term) to be used. Defaults to 'l2' which is the standard regularizer for linear SVM models. 'l1' and 'elasticnet' might bring sparsity to the model (feature selection) not achieva... max_iter int 1657 The maximum number of passes over the training data (aka epochs) It only impacts the behavior in the ``fit`` method, and not the :meth:`partial_fit` method .. versionadded:: 0.19 n_iter_no_change int 44 Number of iterations with no improvement to wait before early stopping .. versionadded:: 0.20 n_jobs int 1 The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details penalty "elasticnet" power_t double 0.5337479304733836 The exponent for inverse scaling learning rate [default 0.5] random_state int 42 The seed of the pseudo random number generator to use when shuffling the data. 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` shuffle bool true Whether or not the training data should be shuffled after each epoch tol float 0.0016762319789051909 The stopping criterion. If it is not None, the iterations will stop when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive epochs .. versionadded:: 0.19 validation_fraction float 0.5789400632046087 The proportion of training data to set aside as validation set for early stopping. Must be between 0 and 1 Only used if early_stopping is True .. versionadded:: 0.20 verbose int 0 The verbosity level warm_start bool false When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution See :term:`the Glossary <warm_start>` Repeatedly calling fit or partial_fit when warm_start is True can result in a different solution than when calling fit a single time because of the way the data is shuffled If a dynamic learning rate is used, the learning rate is adapted depending on the number of samples already seen. Calling ``fit`` resets this counter, while ``partial_fit`` will result in increasing the existing counter 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