Linear Regression

Linear Regression classical method implementation.

This section contains the Linear Regression implementation for regression tasks. Linear Regression is a linear approach to modeling the relationship between a scalar response and one or more explanatory variables.

class TALENT.model.classical_methods.lr.LinearRegressionMethod(args, is_regression)

Bases: classical_methods

construct_model(model_config=None)
fit(data, info, train=True, config=None)
metric(predictions, labels, y_info)
predict(data, info, model_name)
class TALENT.model.classical_methods.lr.LRMethod

Linear Regression method for regression tasks.

Key Features:

  • Uses sklearn’s LinearRegression for regression

  • Linear model for continuous target prediction

  • Supports multiple explanatory variables

  • Automatically handles data preprocessing including normalization and encoding

  • Saves trained model to pickle file for later use

  • Provides coefficient interpretation

Algorithm:

Linear Regression is a linear approach to modeling the relationship between a scalar response and one or more explanatory variables. It assumes a linear relationship between the input variables and the single output variable.

__init__(args, is_regression)

Initialize the Linear 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 Linear Regression model instance.

Parameters:

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

Model Creation:

  • Creates LinearRegression regressor

  • Configures parameters like fit_intercept, normalize, etc.

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

Train the Linear 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 Linear Regression model

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

predict(data, info, model_name)

Make predictions using the trained Linear 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 (continuous values for regression)

Prediction Process:

  1. Data Preprocessing: Applies same preprocessing as training data

  2. Model Loading: Loads the trained Linear Regression model

  3. Prediction: Generates continuous value predictions

  4. Output: Returns predicted values for regression

Evaluation Metrics:

  • For regression: returns MAE, R2, RMSE metrics

References:

[1] Montgomery, D. C., Peck, E. A., & Vining, G. G. (2021). Introduction to linear regression analysis. John Wiley & Sons.