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LinearSVR

Model
DashAI.back.models.scikit_learn.LinearSVR

Support vector regression with a linear kernel for large datasets.

LinearSVR fits a linear function by minimising the epsilon-insensitive loss: predictions within epsilon of the true target incur no penalty, while deviations beyond that are penalised linearly. The regularisation parameter C controls the trade-off between margin width and training error. Because it uses a linear kernel and relies on liblinear internally, LinearSVR scales to large datasets much more efficiently than SVR with a non-linear kernel.

Key hyperparameters include C, epsilon, loss (epsilon-insensitive or squared epsilon-insensitive), fit_intercept, dual, tol, and max_iter. The implementation wraps scikit-learn's LinearSVR.

References​

Parameters​

epsilon : number, default=0.0
Epsilon parameter that specifies the epsilon-tube within which no penalty is associated.
tol : number, default=0.0001
Tolerance for stopping criterion.
C : integer, default=1
Regularization parameter. The strength of the regularization is inversely proportional to C.
loss : string, default=epsilon_insensitive
Specifies the loss function. 'epsilon_insensitive' is the standard SVR loss.
fit_intercept : boolean, default=True
Whether to calculate the intercept for this model.
intercept_scaling : number, default=1.0
When fit_intercept is True, instance vector x becomes [x, self.intercept_scaling] in the primal problem.
dual : boolean, default=True
Select the algorithm to either solve the dual or primal optimization problem.
verbose : integer, default=0
Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm.
random_state, default=None
The seed of the pseudo-random number generator to use when shuffling the data.
max_iter : integer, default=1000
The maximum number of iterations to be run.

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 SklearnLikeRegressor

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​