LR

LR.py

This module implements the Logistic Regression algorithm, a popular supervised learning method used for binary classification tasks. Logistic Regression is a linear model that estimates the probability of a binary response based on one or more predictor variables.

Usage

To use this module, import the LR class and instantiate it with the desired parameters. Then, fit the model to your training data and use it to make predictions on new data.

Example: from engines.contentFilterEngine.traditional_ml_algorithms.LR import LR model = LR() model.fit(X_train, y_train) predictions = model.predict(X_test)

Note

• Ensure that your data is preprocessed appropriately before fitting the model, as Logistic Regression assumes a linear relationship between the input variables and the log-odds of the response.
• This implementation may require additional libraries such as NumPy or SciPy for matrix operations.