The choice between classification and regression in machine learning depends on the nature of the problem and the type of output variable you are trying to predict. Here’s a comprehensive overview of when to use classification and when to use regression:
I. Classification:
When to Use:
1. Categorical Output:
- Classification is appropriate when the target variable is categorical, meaning it falls into distinct classes or categories. Examples include predicting whether an email is spam or not (binary classification) or classifying images of animals into different species (multi-class classification).
2. Discrete Labels:
- When the output variable consists of discrete labels or classes, classification is the natural choice. This applies to tasks like sentiment analysis (positive, neutral, negative) or disease diagnosis (healthy, mild, severe).
3. Binary Decision Problems:
- In scenarios where the goal is to make a binary decision, such as whether a customer will churn or not, classification is suitable. It’s also common in scenarios where the outcome is a yes/no or true/false.
