Linear and Logistic Regression

Requirements Clarification & Assessment

When preparing to answer the interview question about comparing linear and logistic regression, it's crucial to clarify and assess the requirements of the problem:

1. Understand the Context:

   • What is the nature of the dependent variable? Is it continuous or binary/ordinal?
   • What are the specific characteristics of the independent variables? Are they categorical, continuous, or a mix of both?

2. Identify the Objective:

   • Are you aiming to predict a continuous outcome or classify data into categories?
   • Determine if the goal is to understand relationships between variables or to make predictions.

3. Data Characteristics:

   • Assess the distribution of the dependent variable.
   • Consider the linearity between the dependent and independent variables.

4. Scenario Exploration:

   • Explore potential real-world scenarios where each regression type might be applicable.
   • Consider the importance of interpretability and computational efficiency in the context of the problem.

5. Performance Metrics:

   • Define what success looks like for each model type. Is it accuracy, precision, recall, or another metric?
   • Consider how you will evaluate the model's performance in the real-world application.