Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004
Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004
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May 15, 2025
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Author: Mehul Ligade
Originally published on Towards AI.
Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004
If you’re learning Machine Learning and think supervised learning is straightforward, think again.
The moment you start building your first model, you face a decision that most tutorials barely explain: should this be a regression problem or a classification problem?
The difference might seem obvious — until you mess up a project by predicting categories with a regression model or trying to force numeric output into classification buckets.In this article, I will break it all down from the ground up. Not just the textbook definitions, but the thinking process behind choosing the right type of model. You will learn what these terms really mean, how to spot the difference in the wild, and how I personally approach this choice in real-world projects.And as always, no recycled fluff; only experience, insight, and lessons that actually stick.
Now let’s dive in.
Contents
Why This Article Exists
The Real Question Behind Regression and Classification
What Regression Actually Means in ML
A Real-Life Example of Regression
What Classification Means and How It Works
A Real-Life Example of Classification
How to Choose Between ThemEvaluation Metrics You Must Know
What I Learned the Hard Way
Final Thoughts: Don’t Just Choose Models. Understand Problems.
Why This Article Exists
I am writing this because I got it wrong. More than once.
When I first started with supervised learning, I picked models like they were tools in a toolbox. Linear regression for numbers. Logistic regression for yes or no. That was it. End of story.
But then I hit edge cases — datasets that looked like classification but acted like regression. Projects where I used the wrong loss function and got results that were mathematically correct but practically useless. It became clear to me that the distinction between regression and classification is not just about output format. It is about understanding your problem at a deeper level.
So this article is what I wish someone had handed me back then.
—
The Real Question Behind Regression and Classification
Before we define anything, ask yourself this:
What is the nature of the thing I am trying to predict?Am I trying to predict a quantity? Something with measurable distance between values — like price, age, or temperature?Or am I trying to predict a class? A distinct label, category, or group — like cat or dog, spam or not spam, fraud or genuine?That is the fundamental fork in the road.
Regression problems deal with continuous outcomes. You are estimating values on a number line.
Classification problems deal with discrete outcomes. You are assigning input data into one of several predefined buckets.
And every model, loss function, and evaluation metric flows from this initial choice.
—
What Regression Actually Means in ML
Regression is not about graphs or slopes or lines. It is about approximation.
When you use regression, you are asking the model to learn a function that maps input variables to a continuous output — like predicting house price from square footage or predicting someone’s weight based on age and height.
But here’s what matters: there is no “correct” label in a strict sense. There is just closeness. Accuracy in regression is about how far off you are from the actual value. That’s why regression models minimize error — not classification mistakes.
Think about this: if you predict a house price as ₹88,00,000 when it’s actually ₹90,00,000, you are off by 2 lakhs. That’s the loss. That’s what we care about.
You are not trying to get an exact number every time. You are trying to get close and consistently close.
—
A Real-Life Example of Regression
In one of my early projects, I built a system to predict healthcare insurance costs. The dataset included factors like age, BMI, gender, smoking status, and location. The goal was to estimate the cost of a person’s annual premium.
There were no categories. Just numbers — actual premium amounts from previous customers.
This is a textbook regression problem. The output is continuous. The distance between ₹24,000 and ₹26,000 is meaningful. A difference of ₹2,000 is better than a difference of ₹20,000.
My models tried to minimize the error between predicted cost and actual cost. I used RMSEas my main metric. And even though the numbers were not perfect, they got close enough to be valuable for real decision-making.
That is regression. Learning to estimate: not classify.
—
What Classification Means and How It Works
Classification is different. Here, you are predicting categories.
You are not interested in the value of the output — only which group it falls into.This is the kind of learning used in problems like spam detection, loan approval, sentiment analysis, medical diagnosis, and image recognition.
In classification, you are not measuring how close your prediction is — you are measuring whether it is correct or not. There is no halfway.
If you predict that a transaction is “not fraud” and it is actually “fraud,” that is not a 40 percent error — it is a full-blown misclassification. The cost of being wrong can vary, but the format is binary: right or wrong.
Classification models often work by estimating probabilities. For example, a logistic regression model might say, “This email has a 92 percent chance of being spam.” But in the end, it must make a call — spam or not.
The key is to get the categories right.
—
How to Choose Between ThemNow here’s the golden question: how do you decide if your problem is regression or classification?
Ask yourself:
Are you trying to predict a value that falls on a continuous scale? If the answer is yes, it’s probably regression. For example, predicting weight, speed, cost, score, rating, or any other numeric measurement.Are you trying to assign an input to a predefined group? If so, it’s classification. For example, identifying sentiment, detecting objects, predicting diagnoses, or categorizing news articles.
And if you are not sure, here’s a tip: look at your target variable. If it has units — like kilograms, rupees, degrees, or centimeters — it’s probably regression. If it has labels like “positive,” “negative,” “approved,” or “rejected” it’s classification.
—
Evaluation Metrics You Must Know
This is where many people go wrong — including me, at first.
You cannot evaluate regression and classification models the same way.
In regression, we care about how far off the prediction is. Metrics like mean absolute error, mean squared error, or root mean squared error are used. They tell you how close the prediction is to the real value.
In classification, we care about how often the prediction is right. But accuracy alone is not always enough — especially with imbalanced data.
For example, in a fraud detection model where only 1 percent of transactions are actually fraud, a model that says “not fraud” for every case will be 99 percent accurate — and completely useless.
That’s why we use other metrics like precision, recall, F1-score, and AUC. These metrics tell us not just how often we are right, but how we are right — and when it matters.
Knowing the difference between evaluation strategies is just as important as choosing the right model.
—
What I Learned the Hard Way
In one of my earlier models, I was trying to predict how likely someone was to buy a product.
The target column was labeled “purchase likelihood” and had values between 0 and 1. I assumed it was a regression problem. I trained a model using RMSE. The predictions were pretty close.
But then I looked deeper and realized that this target had been generated by a previous model. It was already a probability. What I really needed was a classification decision: “Will buy” or “Will not buy.”
I had treated it like a regression problem when what I really wanted was classification. That mismatch between goal and framing wasted weeks of iteration.
Since then, I always start with the same question: “What decision is this model helping someone make?” That almost always leads me to the right type of problem.
—
Final Thoughts: Don’t Just Choose Models. Understand Problems.
Machine Learning is not about throwing algorithms at data. It is about solving real problems. And that starts with framing those problems the right way.
Choosing between regression and classification is not about picking the most popular model. It is about understanding the shape of the outcome you are trying to predict.
The closer you look at your data — especially your target variable — the better your choices will be. And the better your choices, the more reliable your models become.
This is how I build ML systems. Not just by following tutorials — but by understanding what the model is supposed to do, and why.
—
What Comes Next
In the next few articles, I will dive into model evaluation, error analysis, overfitting, and how I engineer features that actually improve predictions not just accuracy on paper.
As always, I will write from experience. From curiosity. From real-world projects and lessons that stick.
Follow along if you are tired of fluffy articles and want to build Machine Learning systems that actually work.
Find me here:
Twitter: x.com/MehulLigade LinkedIn: linkedin.com/in/mehulcode12Let’s keep learning one layer deeper at a time.
—
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI - Medium
Share this post
#regression #classification #machine #learning #why
Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004
Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004
0 like
May 15, 2025
Share this post
Author: Mehul Ligade
Originally published on Towards AI.
Regression vs Classification in Machine Learning — Why Most Beginners Get This Wrong | M004
If you’re learning Machine Learning and think supervised learning is straightforward, think again.
The moment you start building your first model, you face a decision that most tutorials barely explain: should this be a regression problem or a classification problem?
The difference might seem obvious — until you mess up a project by predicting categories with a regression model or trying to force numeric output into classification buckets.In this article, I will break it all down from the ground up. Not just the textbook definitions, but the thinking process behind choosing the right type of model. You will learn what these terms really mean, how to spot the difference in the wild, and how I personally approach this choice in real-world projects.And as always, no recycled fluff; only experience, insight, and lessons that actually stick.
Now let’s dive in.
📘 Contents
Why This Article Exists
The Real Question Behind Regression and Classification
What Regression Actually Means in ML
A Real-Life Example of Regression
What Classification Means and How It Works
A Real-Life Example of Classification
How to Choose Between ThemEvaluation Metrics You Must Know
What I Learned the Hard Way
Final Thoughts: Don’t Just Choose Models. Understand Problems.
🔴 Why This Article Exists
I am writing this because I got it wrong. More than once.
When I first started with supervised learning, I picked models like they were tools in a toolbox. Linear regression for numbers. Logistic regression for yes or no. That was it. End of story.
But then I hit edge cases — datasets that looked like classification but acted like regression. Projects where I used the wrong loss function and got results that were mathematically correct but practically useless. It became clear to me that the distinction between regression and classification is not just about output format. It is about understanding your problem at a deeper level.
So this article is what I wish someone had handed me back then.
—
🔴 The Real Question Behind Regression and Classification
Before we define anything, ask yourself this:
What is the nature of the thing I am trying to predict?Am I trying to predict a quantity? Something with measurable distance between values — like price, age, or temperature?Or am I trying to predict a class? A distinct label, category, or group — like cat or dog, spam or not spam, fraud or genuine?That is the fundamental fork in the road.
Regression problems deal with continuous outcomes. You are estimating values on a number line.
Classification problems deal with discrete outcomes. You are assigning input data into one of several predefined buckets.
And every model, loss function, and evaluation metric flows from this initial choice.
—
🔴 What Regression Actually Means in ML
Regression is not about graphs or slopes or lines. It is about approximation.
When you use regression, you are asking the model to learn a function that maps input variables to a continuous output — like predicting house price from square footage or predicting someone’s weight based on age and height.
But here’s what matters: there is no “correct” label in a strict sense. There is just closeness. Accuracy in regression is about how far off you are from the actual value. That’s why regression models minimize error — not classification mistakes.
Think about this: if you predict a house price as ₹88,00,000 when it’s actually ₹90,00,000, you are off by 2 lakhs. That’s the loss. That’s what we care about.
You are not trying to get an exact number every time. You are trying to get close and consistently close.
—
🔴 A Real-Life Example of Regression
In one of my early projects, I built a system to predict healthcare insurance costs. The dataset included factors like age, BMI, gender, smoking status, and location. The goal was to estimate the cost of a person’s annual premium.
There were no categories. Just numbers — actual premium amounts from previous customers.
This is a textbook regression problem. The output is continuous. The distance between ₹24,000 and ₹26,000 is meaningful. A difference of ₹2,000 is better than a difference of ₹20,000.
My models tried to minimize the error between predicted cost and actual cost. I used RMSEas my main metric. And even though the numbers were not perfect, they got close enough to be valuable for real decision-making.
That is regression. Learning to estimate: not classify.
—
🔴 What Classification Means and How It Works
Classification is different. Here, you are predicting categories.
You are not interested in the value of the output — only which group it falls into.This is the kind of learning used in problems like spam detection, loan approval, sentiment analysis, medical diagnosis, and image recognition.
In classification, you are not measuring how close your prediction is — you are measuring whether it is correct or not. There is no halfway.
If you predict that a transaction is “not fraud” and it is actually “fraud,” that is not a 40 percent error — it is a full-blown misclassification. The cost of being wrong can vary, but the format is binary: right or wrong.
Classification models often work by estimating probabilities. For example, a logistic regression model might say, “This email has a 92 percent chance of being spam.” But in the end, it must make a call — spam or not.
The key is to get the categories right.
—
🔴 How to Choose Between ThemNow here’s the golden question: how do you decide if your problem is regression or classification?
Ask yourself:
Are you trying to predict a value that falls on a continuous scale? If the answer is yes, it’s probably regression. For example, predicting weight, speed, cost, score, rating, or any other numeric measurement.Are you trying to assign an input to a predefined group? If so, it’s classification. For example, identifying sentiment, detecting objects, predicting diagnoses, or categorizing news articles.
And if you are not sure, here’s a tip: look at your target variable. If it has units — like kilograms, rupees, degrees, or centimeters — it’s probably regression. If it has labels like “positive,” “negative,” “approved,” or “rejected” it’s classification.
—
🔴 Evaluation Metrics You Must Know
This is where many people go wrong — including me, at first.
You cannot evaluate regression and classification models the same way.
In regression, we care about how far off the prediction is. Metrics like mean absolute error, mean squared error, or root mean squared error are used. They tell you how close the prediction is to the real value.
In classification, we care about how often the prediction is right. But accuracy alone is not always enough — especially with imbalanced data.
For example, in a fraud detection model where only 1 percent of transactions are actually fraud, a model that says “not fraud” for every case will be 99 percent accurate — and completely useless.
That’s why we use other metrics like precision, recall, F1-score, and AUC. These metrics tell us not just how often we are right, but how we are right — and when it matters.
Knowing the difference between evaluation strategies is just as important as choosing the right model.
—
🔴 What I Learned the Hard Way
In one of my earlier models, I was trying to predict how likely someone was to buy a product.
The target column was labeled “purchase likelihood” and had values between 0 and 1. I assumed it was a regression problem. I trained a model using RMSE. The predictions were pretty close.
But then I looked deeper and realized that this target had been generated by a previous model. It was already a probability. What I really needed was a classification decision: “Will buy” or “Will not buy.”
I had treated it like a regression problem when what I really wanted was classification. That mismatch between goal and framing wasted weeks of iteration.
Since then, I always start with the same question: “What decision is this model helping someone make?” That almost always leads me to the right type of problem.
—
🔴 Final Thoughts: Don’t Just Choose Models. Understand Problems.
Machine Learning is not about throwing algorithms at data. It is about solving real problems. And that starts with framing those problems the right way.
Choosing between regression and classification is not about picking the most popular model. It is about understanding the shape of the outcome you are trying to predict.
The closer you look at your data — especially your target variable — the better your choices will be. And the better your choices, the more reliable your models become.
This is how I build ML systems. Not just by following tutorials — but by understanding what the model is supposed to do, and why.
—
🔴 What Comes Next
In the next few articles, I will dive into model evaluation, error analysis, overfitting, and how I engineer features that actually improve predictions not just accuracy on paper.
As always, I will write from experience. From curiosity. From real-world projects and lessons that stick.
Follow along if you are tired of fluffy articles and want to build Machine Learning systems that actually work.
📍 Find me here:
Twitter: x.com/MehulLigade LinkedIn: linkedin.com/in/mehulcode12Let’s keep learning one layer deeper at a time.
—
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI - Medium
Share this post
#regression #classification #machine #learning #why
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