How would you explain the difference between linear and logistic regression?

## How would you explain the difference between linear and logistic regression?

**Answer:** "**Linear regression** and **logistic regression** are both used to understand relationships between variables, but they are used for different types of problems.

**1. Type of Problem:**

**Linear Regression**is used when we want to predict a**continuous outcome**. For example, if we want to predict someone’s height based on their age, linear regression will help us because height is a continuous number.**Logistic Regression**is used when we want to predict a**yes or no outcome**. For example, if we want to predict whether someone will buy a product (yes or no), logistic regression is used because the answer is a simple**category**like "yes" or "no" (or 0 and 1).

**2. Model Output:**

**Linear Regression**gives a**number**as the output. For example, it might predict that a house will cost $250,000 based on its size.**Logistic Regression**gives a**probability**that something will happen. For instance, it might tell us there's an 80% chance that a customer will buy a product.

**3. How the Prediction is Made:**

In

**linear regression**, the model fits a straight line through the data points to make predictions. It assumes the relationship between the input and the output is linear (like drawing a straight line on a graph).In

**logistic regression**, the model fits an S-shaped curve (called a sigmoid function) because it’s better suited for**yes or no**outcomes. The curve helps us predict probabilities, which we can then convert into categories like yes/no or true/false.

**4. Examples:**

**Linear Regression Example**: Predicting a person’s weight based on their height.**Logistic Regression Example**: Predicting whether an email is spam (yes or no).

**Conclusion:**

So, to put it simply: **linear regression** is for predicting **numbers** (like prices or heights), while **logistic regression** is for predicting **categories** (like yes/no decisions or pass/fail outcomes)."

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