17653 8323 sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier) sklearn.Pipeline(SimpleImputer,StandardScaler,RandomForestClassifier) 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``. 2020-04-05T22:13:51 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": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}] 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. standardscaler 17480 10987 sklearn.preprocessing._data.StandardScaler sklearn.StandardScaler sklearn.preprocessing._data.StandardScaler 1 openml==0.10.2,sklearn==0.22.1 Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if `with_mean=False`, and `s` is the standard deviation of the training samples or one if `with_std=False`. Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using :meth:`transform`. Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance). For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 a... 2020-01-10T17:13:24 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 copy boolean true If False, try to avoid a copy and do inplace scaling instead This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned with_mean boolean true If True, center the data before scaling This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory with_std boolean true If True, scale the data to unit variance (or equivalently, unit standard deviation). openml-python python scikit-learn sklearn sklearn_0.22.1 simpleimputer 17503 11295 sklearn.impute._base.SimpleImputer sklearn.SimpleImputer sklearn.impute._base.SimpleImputer 13 openml==0.10.2,sklearn==0.22.1 Imputation transformer for completing missing values. 2020-02-08T19:46:35 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 add_indicator boolean false If True, a :class:`MissingIndicator` transform will stack onto output of the imputer's transform. This allows a predictive estimator to account for missingness despite imputation. If a feature has no missing values at fit/train time, the feature won't appear on the missing indicator even if there are missing values at transform/test time. copy boolean true If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if `copy=False`: - If X is not an array of floating values; - If X is encoded as a CSR matrix; - If add_indicator=True fill_value string or numerical value null When strategy == "constant", fill_value is used to replace all occurrences of missing_values If left to the default, fill_value will be 0 when imputing numerical data and "missing_value" for strings or object data types missing_values number NaN The placeholder for the missing values. All occurrences of `missing_values` will be imputed strategy string "mean" The imputation strategy - If "mean", then replace missing values using the mean along each column. Can only be used with numeric data - If "median", then replace missing values using the median along each column. Can only be used with numeric data - If "most_frequent", then replace missing using the most frequent value along each column. Can be used with strings or numeric data - If "constant", then replace missing values with fill_value. Can be used with strings or numeric data .. versionadded:: 0.20 strategy="constant" for fixed value imputation verbose integer 0 Controls the verbosity of the imputer openml-python python scikit-learn sklearn sklearn_0.22.1 randomforestclassifier 17654 8323 sklearn.ensemble._forest.RandomForestClassifier sklearn.RandomForestClassifier sklearn.ensemble._forest.RandomForestClassifier 2 openml==0.10.2,sklearn==0.22.1 A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if `bootstrap=True` (default). 2020-04-05T22:13:51 English sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 bootstrap boolean true Whether bootstrap samples are used when building trees. If False, the whole datset is used to build each tree ccp_alpha non 0.0 Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ``ccp_alpha`` will be chosen. By default, no pruning is performed. See :ref:`minimal_cost_complexity_pruning` for details .. versionadded:: 0.22 class_weight dict null Weights associated with classes in the form ``{class_label: weight}`` If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}] 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))`` The "balanced_subsample" mode is the same as "balanced" except that weights are computed based on the bootstrap sample for every tree grown For multi-output, the weights of each column of y will be multiplied Note that these weights will be multiplied... criterion string "gini" The function to measure the quality of a split. Supported criteria are "gini" for the Gini impurity and "entropy" for the information gain Note: this parameter is tree-specific max_depth integer or None null The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples max_features int "auto" The number of features to consider when looking for the best split: - If int, then consider `max_features` features at each split - If float, then `max_features` is a fraction and `int(max_features * n_features)` features are considered at each split - If "auto", then `max_features=sqrt(n_features)` - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto") - If "log2", then `max_features=log2(n_features)` - If None, then `max_features=n_features` Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than ``max_features`` features max_leaf_nodes int or None null Grow trees with ``max_leaf_nodes`` in best-first fashion Best nodes are defined as relative reduction in impurity If None then unlimited number of leaf nodes max_samples int or float null If bootstrap is True, the number of samples to draw from X to train each base estimator - If None (default), then draw `X.shape[0]` samples - If int, then draw `max_samples` samples - If float, then draw `max_samples * X.shape[0]` samples. Thus, `max_samples` should be in the interval `(0, 1)` .. versionadded:: 0.22 min_impurity_decrease float 1e-07 A node will be split if this split induces a decrease of the impurity greater than or equal to this value The weighted impurity decrease equation is the following:: N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity) where ``N`` is the total number of samples, ``N_t`` is the number of samples at the current node, ``N_t_L`` is the number of samples in the left child, and ``N_t_R`` is the number of samples in the right child ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, if ``sample_weight`` is passed .. versionadded:: 0.19 min_impurity_split float 0 Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf .. deprecated:: 0.19 ``min_impurity_split`` has been deprecated in favor of ``min_impurity_decrease`` in 0.19. The default value of ``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Use ``min_impurity_decrease`` instead min_samples_leaf int 1 The minimum number of samples required to be at a leaf node A split point at any depth will only be considered if it leaves at least ``min_samples_leaf`` training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression - If int, then consider `min_samples_leaf` as the minimum number - If float, then `min_samples_leaf` is a fraction and `ceil(min_samples_leaf * n_samples)` are the minimum number of samples for each node .. versionchanged:: 0.18 Added float values for fractions min_samples_split int 2 The minimum number of samples required to split an internal node: - If int, then consider `min_samples_split` as the minimum number - If float, then `min_samples_split` is a fraction and `ceil(min_samples_split * n_samples)` are the minimum number of samples for each split .. versionchanged:: 0.18 Added float values for fractions min_weight_fraction_leaf float 0.0 The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided n_estimators integer 10 The number of trees in the forest .. versionchanged:: 0.22 The default value of ``n_estimators`` changed from 10 to 100 in 0.22 n_jobs int or None 1 The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`, :meth:`decision_path` and :meth:`apply` are all parallelized over the trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details oob_score bool false Whether to use out-of-bag samples to estimate the generalization accuracy random_state int 1 Controls both the randomness of the bootstrapping of the samples used when building trees (if ``bootstrap=True``) and the sampling of the features to consider when looking for the best split at each node (if ``max_features < n_features``) See :term:`Glossary <random_state>` for details verbose int 0 Controls the verbosity when fitting and predicting warm_start bool false When set to ``True``, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See :term:`the Glossary <warm_start>` openml-python python scikit-learn sklearn sklearn_0.22.1 openml-python python scikit-learn sklearn sklearn_0.22.1