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SVC

Model
DashAI.back.models.scikit_learn.SVC

Support vector machine classifier that maximises the margin between classes.

SVC constructs a maximum-margin hyperplane in a (possibly kernel-transformed) feature space. Training data points that lie on or inside the margin are called support vectors; they fully define the decision boundary. Non-linearly separable problems are addressed by mapping the input space into a higher-dimensional space via kernel functions (linear, polynomial, RBF, or sigmoid).

Regularisation is controlled by C: smaller values allow more misclassified training points in exchange for a wider margin, while larger values enforce a harder margin. The kernel, gamma, degree, and coef0 parameters configure the kernel function. The implementation wraps scikit-learn's SVC.

References​

Parameters​

C : number, default=1.0
The parameter 'C' is a regularization parameter. The strength of the regularization is inversely proportional to C
coef0 : number, default=1.0
The parameter 'coef0' is independent term in kernel function. It is only significant for kernel poly and sigmoid.
degree : number, default=1.0
The 'degree' parameter is only significant for 'poly' kernel.
gamma : string, default=scale
Coefficient for 'rbf', 'poly' and 'sigmoid' kernels.
kernel : string, default=rbf
The 'kernel' parameter is the kernel used in the model.
max_iter : integer, default=-1
The 'max_iter' parameter determines the iteration limit for the solver. It must be of type positive integer or -1 to indicate no limit.
shrinking : boolean, default=True
The 'shrinking' parameter determines whether a shrinking heuristic is used.
tol : number, default=1.0
The parameter 'tol' determines the tolerance for the stop criterion.

Methods​

calculate_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, level: DashAI.back.core.enums.metrics.LevelEnum = <LevelEnum.LAST: 'last'>, log_index: int = None, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None)

Defined on BaseModel

Calculate and save metrics for a given data split and level.

Parameters

split : SplitEnum
The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.
level : LevelEnum
The metric granularity level (LAST, TRIAL, STEP, or BATCH). Defaults to LevelEnum.LAST.
log_index : int, optional
Explicit step index for the metric entry. If None, the next step index is computed automatically. Defaults to None.
x_data : DashAIDataset, optional
Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.
y_data : DashAIDataset, optional
Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

get_metadata(cls) -> Dict[str, Any]

Defined on BaseModel

Get metadata values for the current model.

Returns

Dict[str, Any]
Dictionary containing UI metadata such as the model icon used in the DashAI frontend.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict
Dictionary representing the Json Schema of the component.

load(filename: str) -> None

Defined on SklearnLikeModel

Deserialise a model from disk using joblib.

Parameters

filename : str
Path to the file previously written by :meth:save.

Returns

SklearnLikeModel
The loaded model instance.

predict(self, x_pred: 'DashAIDataset') -> 'ndarray'

Defined on SklearnLikeClassifier

Make a prediction with the model

Parameters

x_pred : DashAIDataset
Dataset with the input data columns.

Returns

np.ndarray
Array with the predicted target values for x_pred

prepare_dataset(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on SklearnLikeModel

Apply the model transformations to the dataset.

Parameters

dataset : DashAIDataset
The dataset to be transformed.
is_fit : bool, optional
If True, the method will fit encoders on the data. If False, will apply previously fitted encoders.

Returns

DashAIDataset
The prepared dataset ready to be converted to an accepted format in the model.

prepare_output(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on SklearnLikeModel

Prepare output targets using Label encoding.

Parameters

dataset : DashAIDataset
The output dataset to be transformed.
is_fit : bool, optional
If True, fit the encoder. If False, use existing encodings.

Returns

DashAIDataset
Dataset with categorical columns converted to integers.

save(self, filename: str) -> None

Defined on SklearnLikeModel

Serialise the model to disk using joblib.

Parameters

filename : str
Destination file path where the model will be written.

train(self, x_train, y_train, x_validation=None, y_validation=None)

Defined on SklearnLikeModel

Train the sklearn model on the provided dataset.

Parameters

x_train : DashAIDataset
The input features for training.
y_train : DashAIDataset
The target labels for training.
x_validation : DashAIDataset, optional
Validation input features (unused in sklearn models). Defaults to None.
y_validation : DashAIDataset, optional
Validation target labels (unused in sklearn models). Defaults to None.

Returns

BaseModel
The fitted scikit-learn estimator (self).

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict
A dictionary with the data provided by the user to initialize the model.

Returns

dict
A validated dictionary with the necessary objects.

Compatible with​