Logistic Regression

Logistic Regression classical method implementation.

This section contains the Logistic Regression implementation for classification tasks. Logistic Regression is a linear model for classification that uses a logistic function to model the probability of a certain class or event.

class TALENT.model.classical_methods.logreg.LogRegMethod(args, is_regression)

Bases: classical_methods

construct_model(model_config=None)
fit(data, info, train=True, config=None)
predict(data, info, model_name)
class TALENT.model.classical_methods.logreg.LogRegMethod

Logistic Regression method for classification tasks.

Key Features:

  • Uses sklearn’s LogisticRegression for classification

  • Linear model with logistic function for probability estimation

  • Supports both binary and multiclass classification

  • Automatically handles data preprocessing including normalization and encoding

  • Saves trained model to pickle file for later use

  • Provides probability predictions

Algorithm:

Logistic Regression is a linear model for classification that uses a logistic function to model the probability of a certain class or event. It is a special case of linear regression where the dependent variable is categorical.

__init__(args, is_regression)

Initialize the Logistic Regression method.

Parameters:

  • args (object) – Configuration arguments containing model settings

  • is_regression (bool) – Whether the task is regression (True) or classification (False)

construct_model(model_config=None)

Construct the Logistic Regression model instance.

Parameters:

  • model_config (dict, optional) – Model configuration parameters for Logistic Regression

Model Creation:

  • Creates LogisticRegression classifier

  • Configures parameters like regularization, solver, etc.

fit(data, info, train=True, config=None)

Train the Logistic Regression model on the provided data.

Parameters:

  • data (tuple) – Tuple containing (N, C, y) where N is numerical features, C is categorical features, y is labels

  • info (dict) – Dataset information

  • train (bool, default=True) – Whether to train the model or just load from checkpoint

  • config (dict, optional) – Additional configuration parameters

Returns:

  • time_cost (float) – Training time in seconds

Training Process:

  1. Data Preprocessing: Handles missing values, categorical encoding, normalization

  2. Model Training: Fits the Logistic Regression model

  3. Model Saving: Saves the trained model to disk for later use

predict(data, info, model_name)

Make predictions using the trained Logistic Regression model.

Parameters:

  • data (tuple) – Tuple containing (N, C, y) where N is numerical features, C is categorical features, y is labels

  • info (dict) – Dataset information

  • model_name (str) – Name of the model for saving/loading

Returns:

  • test_logit (array-like) – Test predictions (probabilities for classification)

Prediction Process:

  1. Data Preprocessing: Applies same preprocessing as training data

  2. Model Loading: Loads the trained Logistic Regression model

  3. Prediction: Generates probability predictions

  4. Output: Returns probabilities for classification

Evaluation Metrics:

  • For classification: returns Accuracy, Avg_Precision, Avg_Recall, F1 metrics

References:

[1] Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.