GaussianNB

Model

DashAI.back.models.scikit_learn.GaussianNB

---

Gaussian Naïve Bayes classifier based on Bayes' theorem.

GaussianNB models the likelihood of features as Gaussian distributions per class. It estimates the mean and variance of each feature in each class from the training data, then uses Bayes' theorem to compute the posterior class probability. Despite the strong independence assumption, it often performs well and is very fast.

Key hyperparameter: var_smoothing (additive variance for numerical stability). The implementation wraps scikit-learn's GaussianNB.

References

• [1] https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html

Parameters

var_smoothing : number, default=1e-09

Portion of the largest variance of all features that is added to variances for calculation stability.

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, fold_index: int = None, inner_fold_index: int = None)

Defined on BaseModel

Calculate metrics for a data split and save them to the database.

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.

Returns

Dict[str, float] or None

What was written, so a caller that also wants the numbers does not have to score the same split twice. None when nothing was written: no run to write against, or nothing to score.

compute_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None) -> Optional[Dict[str, float]]

Defined on BaseModel

Score a data split with this model's metrics, without persisting.

Parameters

split : SplitEnum

The data split to evaluate. Defaults to SplitEnum.VALIDATION.

x_data : DashAIDataset, optional

Input features. Defaults to the split stored on the model.

y_data : DashAIDataset, optional

Target labels. Defaults to the split stored on the model.

Returns

Dict[str, float] or None

Metric name to score. A metric that scored a non-finite value is absent from the mapping rather than present with a NaN, so the mapping is empty when every metric returned one. None when there is nothing to score at all: no metrics configured, or no data for this split. Callers that need a particular metric must therefore check that it is there.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str

Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential

An instance of the requested credential component.

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

predict_prepared(self, features) -> 'ndarray'

Defined on SklearnLikeClassifier

Predict from a feature matrix already in the model's feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray

Feature matrix as produced by prepare_dataset.

Returns

np.ndarray

Class probability matrix.

predict_proba_prepared(self, features) -> 'ndarray'

Defined on SklearnLikeClassifier

Return class probabilities for an already prepared feature matrix.

Parameters

features : pandas.DataFrame or numpy.ndarray

Feature matrix as produced by prepare_dataset.

Returns

np.ndarray

Class probability matrix.

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

Defined on CategoricalEncoderMixin

Encode categorical feature columns into a numeric representation.

Parameters

dataset : DashAIDataset

The input dataset to preprocess.

is_fit : bool

If True, fit the encoders on the data. If False, apply previously fitted encoders. Defaults to False.

Returns

DashAIDataset

The dataset with categorical columns converted to numeric columns.

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

Defined on CategoricalEncoderMixin

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

• TabularClassificationTask