Classification algorithms

Warning

Some capabilities may require the installation of GEMSEO with all its features and some others may depend on plugins.

Note

All the features of the wrapped libraries may not be exposed through GEMSEO.

KNNClassifier

Module: gemseo.mlearning.classification.knn

Required parameters

  • data : IODataset

    The learning dataset.

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.

  • n_neighbors : int, optional

    The number of neighbors.

    By default it is set to 5.

  • 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.

  • transformer : TransformerType, optional

    The strategies to transform the variables. The values are instances of 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 Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

    By default it is set to {}.

  • **parameters : int | str

    The parameters of the machine learning algorithm.

RandomForestClassifier

Module: gemseo.mlearning.classification.random_forest

Required parameters

  • data : IODataset

    The learning dataset.

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.

  • n_estimators : int, optional

    The number of trees in the forest.

    By default it is set to 100.

  • 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.

  • 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.

  • transformer : TransformerType, optional

    The strategies to transform the variables. The values are instances of 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 Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

    By default it is set to {}.

  • **parameters : int | float | bool | str | None

    The parameters of the machine learning algorithm.

SVMClassifier

Module: gemseo.mlearning.classification.svm

Required parameters

  • data : IODataset

    The learning dataset.

Optional parameters

  • C : float, optional

    The inverse L2 regularization parameter. Higher values give less regularization.

    By default it is set to 1.0.

  • 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.

  • 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.

  • 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.

  • probability : bool, optional

    Whether to enable the probability estimates. The algorithm is faster if set to False.

    By default it is set to False.

  • 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.

  • transformer : TransformerType, optional

    The strategies to transform the variables. The values are instances of 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 Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

    By default it is set to {}.

  • **parameters : int | float | bool | str | None

    The parameters of the machine learning algorithm.