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

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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