Skip to main content

LinearSVCClassifier

Model
DashAI.back.models.scikit_learn.LinearSVCClassifier

Linear SVC classifier with Platt-scaling calibration for class probabilities.

LinearSVC uses a linear kernel and is trained with coordinate descent, making it considerably faster than kernel SVC on large datasets. Because LinearSVC does not expose predict_proba natively, this wrapper fits a CalibratedClassifierCV with sigmoid calibration so that probability estimates are available to the DashAI evaluation pipeline.

Key hyperparameters include C (regularisation), loss, max_iter, and fit_intercept. The implementation wraps scikit-learn's LinearSVC.

References​

Parameters​

C : number, default=1.0
Regularisation parameter. The strength of the regularisation is inversely proportional to C. Must be strictly positive.
loss : string, default=squared_hinge
Specifies the loss function. 'squared_hinge' is the default; 'hinge' is the standard SVM loss.
max_iter : integer, default=1000
The maximum number of iterations to be run.
tol : number, default=0.0001
Tolerance for stopping criteria.
fit_intercept : boolean, default=True
Whether to calculate the intercept for this model. If False, the data is expected to be already centred.
random_state, default=None
The seed of the pseudo-random number generator. Pass an int for reproducible output, or None to not set a specific seed.

Methods​

predict(self, x_pred) -> 'ndarray'

Defined on LinearSVCClassifier

Return class-probability matrix using the calibrated model.

Parameters

x_pred : DashAIDataset or pd.DataFrame
Input data.

Returns

np.ndarray
Class probability matrix.

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

Defined on LinearSVCClassifier

Train using CalibratedClassifierCV to expose predict_proba.

Parameters

x_train : DashAIDataset
The input features for training.
y_train : DashAIDataset
The target labels for training.
x_validation : DashAIDataset, optional
Unused (sklearn models ignore validation split).
y_validation : DashAIDataset, optional
Unused.

Returns

self

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.

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.

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​