Between Classification and Regression Tasks

Requirements Clarification & Assessment

Before diving into the solution, it's essential to clarify and assess the requirements of distinguishing between classification and regression tasks. This involves understanding the context and scope of the question in the interview setting:

1. Objective Understanding:

   • Classification: Identify the need to predict discrete categories or class labels from input data.
   • Regression: Recognize the requirement to predict continuous numerical values.

2. Data Type Assessment:

   • Classification: Determine if the target variable is categorical, which could be binary, nominal, or ordinal.
   • Regression: Check if the target variable is continuous, involving real numbers.

3. Evaluation Criteria:

   • Classification: Understand the evaluation metrics such as accuracy, precision, recall, F1 score, and ROC-AUC.
   • Regression: Identify metrics like MSE, RMSE, MAE, R-squared, and Adjusted R-squared.

4. Understanding Use Cases:

   • Classification: Grasp common applications like spam detection, medical diagnosis, and image recognition.
   • Regression: Comprehend scenarios like price prediction, weather forecasting, and sales estimation.