Classification algorithms

Warning

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

Note

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

KNNClassifier

Module: gemseo.mlearning.classification.knn

Required parameters

• data : Dataset

  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 : Mapping[str, TransformerType] | None, 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 None, do not transform the variables.

  By default it is set to None.

• **parameters : int | str

  The parameters of the machine learning algorithm.

RandomForestClassifier

Module: gemseo.mlearning.classification.random_forest

Required parameters

• data : Dataset

  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.

• transformer : Mapping[str, TransformerType] | None, 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 None, do not transform the variables.

  By default it is set to None.

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

  The parameters of the machine learning algorithm.

SVMClassifier

Module: gemseo.mlearning.classification.svm

Required parameters

• data : Dataset

  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.

• 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.

• transformer : Mapping[str, TransformerType] | None, 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 None, do not transform the variables.

  By default it is set to None.

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

  The parameters of the machine learning algorithm.