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TfIdfLogRegTextClassificationModel

Model
DashAI.back.models.scikit_learn.TfIdfLogRegTextClassificationModel

TF-IDF vectorizer combined with Logistic Regression for text classification.

This model converts raw text into TF-IDF feature vectors using scikit-learn's TfidfVectorizer with a configurable n-gram range and IDF weighting, then trains a LogisticRegression classifier on the resulting sparse matrix. It is a strong baseline for text classification tasks, particularly when training data is limited or computational resources are constrained.

References​

Parameters​

ngram_min_n : integer, default=1
Minimum n-gram size for the TF-IDF vectorizer (≥ 1).
ngram_max_n : integer, default=1
Maximum n-gram size for the TF-IDF vectorizer (≥ 1).
use_idf : boolean, default=True
Enable inverse-document-frequency re-weighting.
sublinear_tf : boolean, default=False
Apply sublinear TF scaling (replace TF with 1 + log(TF)).
C : number, default=1.0
Regularization parameter for logistic regression. Smaller values mean stronger regularization.
max_iter : integer, default=1000
Maximum number of iterations for the logistic regression solver.
solver : string, default=lbfgs
Optimization algorithm for logistic regression.

Methods​

load(filename: Union[str, ForwardRef('Path')])

Defined on TfIdfLogRegTextClassificationModel

Restore a model instance from disk.

Parameters

filename : str
Path where the model was previously saved.

Returns

Any
The restored model instance.

predict(self, x)

Defined on TfIdfLogRegTextClassificationModel

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

Defined on TfIdfLogRegTextClassificationModel

Hook for model-specific preprocessing of output targets.

Parameters

dataset : DashAIDataset
The output dataset (target labels) to preprocess.
is_fit : bool
Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset
The preprocessed output dataset.

save(self, filename: Union[str, ForwardRef('Path')]) -> None

Defined on TfIdfLogRegTextClassificationModel

Store the model to disk.

Parameters

filename : str
Path where the model will be saved.

train(self, x, y, x_validation=None, y_validation=None)

Defined on TfIdfLogRegTextClassificationModel

Train the model with the provided data.

Parameters

x_train : DashAIDataset
The input features for training.
y_train : DashAIDataset
The target labels for training.
x_validation : DashAIDataset, optional
Input features for validation. Defaults to None.
y_validation : DashAIDataset, optional
Target labels for validation. Defaults to None.

Returns

BaseModel
The trained model instance.

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.

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

Defined on BaseModel

Hook for model-specific preprocessing of input features.

Parameters

dataset : DashAIDataset
The input dataset to preprocess.
is_fit : bool
Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset
The preprocessed dataset ready to be fed into the model.

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​