Today, I want to talk to you about something important in machine learning: **Why Linear Regression Cannot Be Used for Classification**. If you’re just starting out, it can be confusing to know when to use certain algorithms. So, let me break it down for you in simple terms and show you why **linear regression**, which works great for some tasks, doesn’t really fit when you’re dealing with **classification problems**.

By the end of this, you’ll have a clear understanding of why we need other methods for classification.

So, without any further ado, let’s get started-

**Why Linear Regression Cannot Be Used for Classification**

**What is Linear Regression?**

First, let’s talk about **linear regression**. Linear regression is a way to make predictions when your target (the thing you want to predict) is a **number**. For example, if you’re predicting house prices, temperature, or sales numbers, linear regression is your friend.

**How Linear Regression Works**

In linear regression, we try to draw a **straight line** that best fits the data points we have. Imagine you have some dots on a graph (those dots are your data points), and you try to draw a line that’s as close as possible to all the dots. The formula for this line looks like this:

Y=mX+b

Where:

**Y**is the target value (what you want to predict)**X**is the input feature (the thing you know)**m**is the slope of the line**b**is the intercept (where the line crosses the Y-axis)

Linear regression is great when you want to predict continuous values like height, weight, or prices.

**Example in Python**

This is a simple example of linear regression using Python and the `Scikit-Learn`

library:

```
from sklearn.linear_model import LinearRegression
import numpy as np
# Sample data (X = input, y = target values)
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([2, 4, 5, 4, 5])
# Create a linear regression model
model = LinearRegression()
# Train the model
model.fit(X, y)
# Make predictions
predictions = model.predict(X)
print("Predicted values:", predictions)
```

**What is Classification?**

Now, let’s talk about **classification**. In classification problems, you’re not predicting numbers. Instead, you’re trying to put things into **categories** or **classes**. For example, let’s say you want to figure out if an email is spam or not spam. That’s a **classification** problem because there are only two possible answers (classes): “spam” or “not spam”.

**Types of Classification Problems**

There are different types of classification problems. Let me quickly explain a few:

**Binary classification**: This is when there are only two possible outcomes (like spam vs. not spam).**Multiclass classification**: This is when there are more than two outcomes. For example, you might want to classify a type of flower as**setosa**,**versicolor**, or**virginica**.**Multilabel classification**: This is when each instance can belong to more than one class. For example, a movie could belong to both the “comedy” and “action” genres at the same time.

**The Difference Between Regression and Classification**

To make things clearer, let’s compare **regression** and **classification**:

**Regression**predicts continuous values (e.g., predicting a price).**Classification**predicts discrete categories (e.g., predicting if an email is spam or not).

Or you can say:

- In regression, you’re working with numbers.
- In classification, you’re working with labels or categories.

**Why Linear Regression Fails in Classification**

So, why can’t we just use linear regression to solve classification problems? There are several reasons, and I’ll explain them in simple terms.

**Problem with Output Values**

Linear regression predicts **any number** on a continuous scale. But in classification, you only want specific categories. For example, if you’re trying to classify whether an email is spam (1) or not spam (0), linear regression might predict something like **0.6** or **-1.2**. These values don’t make sense because we need the result to be **either 0 or 1** (spam or not spam), not some number in between.

Example: Incorrect Predictions from Linear Regression

```
# Binary classification problem
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([0, 0, 1, 1, 1]) # Classification labels (binary: 0 or 1)
# Linear Regression for classification (not ideal)
model = LinearRegression()
model.fit(X, y)
predictions = model.predict(X)
print("Predicted values:", predictions)
```

This code will give us predictions that aren’t exactly 0 or 1, but values like 0.3 or 1.2, which aren’t valid for classification tasks.

**Poor Boundaries for Class Separation**

When using linear regression for classification, it tries to draw a **straight line** to separate the classes. However, real-life classification problems are often more complex. The line that separates classes (called a **decision boundary**) may not be straight. Linear regression just doesn’t handle this complexity well, which means it doesn’t do a good job at separating classes.

**Sensitivity to Outliers**

Linear regression is very sensitive to **outliers** (data points that are far from others). In classification, you might have a few unusual points in your data, and linear regression can be easily affected by them, leading to poor predictions.

**Probabilities vs. Categories**

Another issue is that classification often deals with **probabilities**. You might want to know how likely it is that an email is spam. Linear regression doesn’t naturally give you probabilities. It just gives you a number, which doesn’t help much when you want to know how confident you are in your prediction.

**Alternatives to Linear Regression for Classification**

Now that we know why linear regression isn’t a good fit for classification, what should we use instead? Luckily, there are some great algorithms that are specifically designed for classification.

**Logistic Regression**

**Logistic regression** is one of the most popular methods for classification. Even though it has “regression” in the name, it’s used for classification tasks. Here’s how it works:

- Instead of predicting a continuous number, logistic regression predicts the
**probability**that something belongs to a certain class. - It uses a special function (called the
**sigmoid function**) to convert its output into a value between 0 and 1, which can be used for binary classification.

Sigmoid Function:

This is a Python example using logistic regression for binary classification:

```
from sklearn.linear_model import LogisticRegression
# Binary classification data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([0, 0, 1, 1, 1])
# Logistic Regression model
log_reg = LogisticRegression()
log_reg.fit(X, y)
# Predictions
predictions = log_reg.predict(X)
print("Predicted labels:", predictions)
```

**Decision Trees**

**Decision trees** are another good option. They work by splitting the data into smaller and smaller groups, based on the features of the data. At the end, the tree gives you a decision based on these splits (e.g., “spam” or “not spam”).

**Random Forest**

**Random forests** are like decision trees, but better! Instead of just one tree, random forests create many trees and combine their decisions. This usually gives you a more accurate result.

**Support Vector Machines (SVM)**

**SVMs** are powerful algorithms that try to find the best line (or hyperplane) that separates different classes. They can work with both linear and non-linear boundaries, making them useful for complex classification tasks.

**Neural Networks**

For more complex classification tasks, especially when you have a lot of data, **neural networks** are a great option. They’re used in deep learning and can handle very complex patterns.

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I hope now you understand **“Why Linear Regression Cannot Be Used for Classification?”. **Now, it’s time to wrap up this article.

**Conclusion**

In this article, I aimed to answer your question: **Why Linear Regression Cannot Be Used for Classification**. I hope this guide helps you get started on your journey. If you have any doubts or questions, don’t hesitate to ask me in the comment section!

All the Best!

Enjoy Learning!

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### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.