****parameters** (*Union[int,str]*)
The parameters of the machine learning algorithm.
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**data** (*Dataset*)
The learning dataset.
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**input_names** (*Optional[Iterable[str]]*)
The names of the input variables. If None, consider all input variables mentioned in the learning dataset.
By default it is set to None.
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**n_neighbors** (*int*)
The number of neighbors.
By default it is set to 5.
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**output_names** (*Optional[Iterable[str]]*)
The names of the output variables. If None, consider all input variables mentioned in the learning dataset.
By default it is set to None.
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**transformer** (*Optional[TransformerType]*)
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 None, do not transform the variables.
By default it is set to None.
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.. _RandomForestClassifier_options:
RandomForestClassifier
----------------------
Module: :class:`gemseo.mlearning.classification.random_forest`
Here are the options available in |g|:
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Options
****parameters** (*Optional[Union[int,float,bool,str]]*)
The parameters of the machine learning algorithm.
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**data** (*Dataset*)
The learning dataset.
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**input_names** (*Optional[Iterable[str]]*)
The names of the input variables. If None, consider all input variables mentioned in the learning dataset.
By default it is set to None.
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**n_estimators** (*int*)
The number of trees in the forest.
By default it is set to 100.
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**output_names** (*Optional[Iterable[str]]*)
The names of the output variables. If None, consider all input variables mentioned in the learning dataset.
By default it is set to None.
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**transformer** (*Optional[TransformerType]*)
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 None, do not transform the variables.
By default it is set to None.
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.. _SVMClassifier_options:
SVMClassifier
-------------
Module: :class:`gemseo.mlearning.classification.svm`
Here are the options available in |g|:
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Options
****parameters** (*Optional[Union[int,float,bool,str]]*)
The parameters of the machine learning algorithm.
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**C** (*float*)
The inverse L2 regularization parameter. Higher values give less regularization.
By default it is set to 1.0.
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**data** (*Dataset*)
The learning dataset.
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**input_names** (*Optional[Iterable[str]]*)
The names of the input variables. If None, consider all input variables mentioned in the learning dataset.
By default it is set to None.
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**kernel** (*Optional[str,Callable]*)
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.
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**output_names** (*Optional[Iterable[str]]*)
The names of the output variables. If None, consider all input variables mentioned in the learning dataset.
By default it is set to None.
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**probability** (*bool*)
Whether to enable the probability estimates. The algorithm is faster if set to False.
By default it is set to False.
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**transformer** (*Optional[TransformerType]*)
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 None, do not transform the variables.
By default it is set to None.
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