# Options for Classification algorithms¶

## KNNClassifier¶

Here are the options available in GEMSEO:

Options
• **parameters (Union[int,str])

The parameters of the machine learning algorithm.

• data (Dataset)

The learning dataset.

• 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.

• n_neighbors (int)

The number of neighbors.

By default it is set to 5.

• 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.

• transformer (Optional[TransformerType])

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.

## RandomForestClassifier¶

Here are the options available in GEMSEO:

Options
• **parameters (Optional[Union[int,float,bool,str]])

The parameters of the machine learning algorithm.

• data (Dataset)

The learning dataset.

• 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.

• n_estimators (int)

The number of trees in the forest.

By default it is set to 100.

• 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.

• transformer (Optional[TransformerType])

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.

## SVMClassifier¶

Here are the options available in GEMSEO:

Options
• **parameters (Optional[Union[int,float,bool,str]])

The parameters of the machine learning algorithm.

• C (float)

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

By default it is set to 1.0.

• data (Dataset)

The learning dataset.

• 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.

• 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.

• 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.

• probability (bool)

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

By default it is set to False.

• transformer (Optional[TransformerType])

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.