The realm of machine learning offers a diverse toolkit of techniques, each suited to different types of problems. A common point of discussion, especially in educational settings, revolves around the relationship between regression and classification problems. A recent claim from a teaching assistant (TA) suggested that regression problems should “often be cast into classification problems,” particularly when employing multi-loss functions, supposedly because classification algorithms are superior. This perspective, however, warrants careful examination and, in many cases, outright rebuttal.
The core issue with transforming a regression problem into a classification one lies in the fundamental nature of the data and the information we risk discarding. Regression problems inherently deal with continuous output variables, where the goal is to predict a specific numerical value. Think of predicting house prices, temperature, or stock market fluctuations. These are all scenarios where the magnitude of the prediction matters, and the output exists on a continuous scale.
Classification, on the other hand, is designed for categorical outputs. We aim to assign data points to predefined classes or categories. Examples include classifying emails as spam or not spam, identifying images of cats versus dogs, or diagnosing diseases based on symptoms. Here, the output is discrete, belonging to a specific group.
The TA’s suggestion to convert regression to classification typically involves binning or discretizing the continuous output range. For instance, if we are predicting temperature, we might divide the range into bins like “cold,” “mild,” “warm,” and “hot.” While seemingly simplifying the problem, this approach introduces significant drawbacks.
Firstly, and most critically, it discards the inherent ordinal nature and granularity of the regression problem. Consider temperature prediction again. If we bin temperatures, the difference between 20°C and 21°C, both potentially falling into the same “mild” category, is lost in terms of model output. In regression, this subtle difference is captured and can be crucial. Furthermore, if using more than two bins, the problem morphs into an ordinal classification problem, not a flat classification, acknowledging an order between categories, but still losing the continuous information.
Secondly, the assertion that classification algorithms are inherently “better” than regression algorithms is a broad generalization and often inaccurate. The “best” algorithm is always context-dependent and relies heavily on the specific dataset and problem at hand. Both regression and classification boast a wide array of powerful algorithms, and their effectiveness is determined by factors like data distribution, feature relevance, and model tuning, not a blanket superiority of one over the other.
While generally ill-advised, are there scenarios where casting regression as classification might hold merit? Potentially, in very specific applications where the ultimate decision is categorical, and precision in the continuous output is secondary. The example of temperature prediction and deciding whether to wear a coat illustrates this point. If the primary goal is solely to decide “coat” or “no coat,” and temperature is merely an intermediate variable, discretizing temperature around a decision threshold might be acceptable. In such a case, extreme inaccuracies in regression far from the decision boundary might be less consequential than accuracy near the critical threshold. However, even in such scenarios, focusing on improving the regression model’s accuracy, especially around critical decision points, often proves to be a more robust and informative approach. Furthermore, ordinal regression models often present a more nuanced and information-preserving intermediate approach compared to outright discarding the continuous nature of the data.
In conclusion, while creative problem-solving in machine learning is encouraged, the idea of routinely converting regression problems into classification problems based on a perceived algorithmic advantage is fundamentally flawed. It often leads to information loss, misrepresents the problem’s nature, and overlooks the strengths of regression techniques. Understanding the core differences between regression and classification, and choosing the approach that aligns with the problem’s inherent characteristics, remains paramount for effective and insightful machine learning practice.
Further Reading:
- Reducing Regression to Classification
- How to convert regression into classification?
