Logistic vs. Linear Regression Models

Requirements Clarification & Assessment

1. Understanding the Problem Statement:

   • Logistic Regression: Used for binary classification problems where the outcome is categorical (e.g., yes/no, true/false).
   • Linear Regression: Used for predicting continuous numerical outcomes (e.g., predicting sales, temperature).

2. Key Differences:

   • Nature of Outcome: Logistic regression predicts probabilities of class membership, while linear regression predicts continuous values.
   • Model Interpretation: Logistic regression coefficients are interpreted as changes in log odds, while linear regression coefficients represent changes in the outcome variable.

3. Evaluation Metrics:

   • Logistic Regression: Requires classification metrics like accuracy, precision, recall, F1-score, ROC-AUC.
   • Linear Regression: Evaluated using regression metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared.

4. Model Requirements:

   • Data Suitability: Ensure data meets assumptions for each model type (e.g., linearity, independence).