How to Convert Regression Into Classification?

Answer: Convert regression into classification by defining specific thresholds or ranges for the continuous output, assigning each to a discrete class label.

Converting a regression problem into a classification task involves discretizing the continuous output variable into categorical classes. This process allows for the prediction of discrete labels instead of continuous values. The approach can be particularly useful in scenarios where the specific value is less important than the value’s range or category.

Steps to Convert Regression to Classification:

1. Define Thresholds/Ranges: Determine the intervals or thresholds that differentiate between the categories. These should be based on the distribution of the target variable, domain knowledge, or specific requirements of the problem.
2. Assign Class Labels: Map each range or interval of the continuous variable to a discrete class label. This involves creating a new categorical variable where each value represents a class corresponding to a specific range of the original continuous variable.
3. Update Dataset: Transform the continuous target variable in the dataset into the new categorical variable with class labels.
4. Apply Classification Model: Use a classification algorithm (e.g., logistic regression, decision trees, SVM) to predict the categorical labels.

Conclusion:

Converting regression into classification by discretizing the continuous output into categories enables the application of classification algorithms to predict discrete outcomes. This approach is useful when the interest lies in identifying the category of the outcome rather than its precise value, making the problem more interpretable or aligning with specific decision-making processes.