.. Copyright 2021 IRT Saint-Exupéry, https://www.irt-saintexupery.com This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. .. _gen_classification_algos: Classification algorithms ========================= .. warning:: Some capabilities may require the :ref:`installation ` of |g| with :ref:`all its features ` and some others may depend on :ref:`plugins `. .. note:: All the features of the wrapped libraries may not be exposed through |g|. .. raw:: html .. _KNNClassifier_options: KNNClassifier ------------- Module: :class:`gemseo.mlearning.classification.knn` .. raw:: html
Required parameters
  • **data** : *IODataset* The learning dataset. .. raw:: html
Optional parameters
  • **input_names** : *Iterable[str] | None, optional* The names of the input variables. If ``None``, consider all the input variables of the learning dataset. By default it is set to None. .. raw:: html
  • **n_neighbors** : *int, optional* The number of neighbors. By default it is set to 5. .. raw:: html
  • **output_names** : *Iterable[str] | None, optional* The names of the output variables. If ``None``, consider all the output variables of the learning dataset. By default it is set to None. .. raw:: html
  • **transformer** : *TransformerType, optional* The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If :attr:`.IDENTITY`, do not transform the variables. By default it is set to {}. .. raw:: html
  • ****parameters** : *int | str* The parameters of the machine learning algorithm. .. raw:: html
.. _RandomForestClassifier_options: RandomForestClassifier ---------------------- Module: :class:`gemseo.mlearning.classification.random_forest` .. raw:: html
Required parameters
  • **data** : *IODataset* The learning dataset. .. raw:: html
Optional parameters
  • **input_names** : *Iterable[str] | None, optional* The names of the input variables. If ``None``, consider all the input variables of the learning dataset. By default it is set to None. .. raw:: html
  • **n_estimators** : *int, optional* The number of trees in the forest. By default it is set to 100. .. raw:: html
  • **output_names** : *Iterable[str] | None, optional* The names of the output variables. If ``None``, consider all the output variables of the learning dataset. By default it is set to None. .. raw:: html
  • **random_state** : *int | None, optional* The random state passed to the random number generator. Use an integer for reproducible results. By default it is set to 0. .. raw:: html
  • **transformer** : *TransformerType, optional* The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If :attr:`.IDENTITY`, do not transform the variables. By default it is set to {}. .. raw:: html
  • ****parameters** : *int | float | bool | str | None* The parameters of the machine learning algorithm. .. raw:: html
.. _SVMClassifier_options: SVMClassifier ------------- Module: :class:`gemseo.mlearning.classification.svm` .. raw:: html
Required parameters
  • **data** : *IODataset* The learning dataset. .. raw:: html
Optional parameters
  • **C** : *float, optional* The inverse L2 regularization parameter. Higher values give less regularization. By default it is set to 1.0. .. raw:: html
  • **input_names** : *Iterable[str] | None, optional* The names of the input variables. If ``None``, consider all the input variables of the learning dataset. By default it is set to None. .. raw:: html
  • **kernel** : *str | Callable | None, optional* The name of the kernel or a callable for the SVM. Examples: "linear", "poly", "rbf", "sigmoid", "precomputed" or a callable. By default it is set to rbf. .. raw:: html
  • **output_names** : *Iterable[str] | None, optional* The names of the output variables. If ``None``, consider all the output variables of the learning dataset. By default it is set to None. .. raw:: html
  • **probability** : *bool, optional* Whether to enable the probability estimates. The algorithm is faster if set to False. By default it is set to False. .. raw:: html
  • **random_state** : *int | None, optional* The random state passed to the random number generator. Use an integer for reproducible results. By default it is set to 0. .. raw:: html
  • **transformer** : *TransformerType, optional* The strategies to transform the variables. The values are instances of :class:`.Transformer` while the keys are the names of either the variables or the groups of variables, e.g. ``"inputs"`` or ``"outputs"`` in the case of the regression algorithms. If a group is specified, the :class:`.Transformer` will be applied to all the variables of this group. If :attr:`.IDENTITY`, do not transform the variables. By default it is set to {}. .. raw:: html
  • ****parameters** : *int | float | bool | str | None* The parameters of the machine learning algorithm. .. raw:: html