Hello mates! It’s Day-3 of studying machine studying. Right now, we’ll discuss Logistic Regression. It’s a approach to train a pc to reply yes-or-no questions — like “Will it rain?” or “Is that this a canine?” I’ll clarify it in a brilliant simple manner with examples, so that you perceive the way it works, the way it guesses, and the way it learns. Let’s begin!
What’s Logistic Regression?
Logistic Regression helps a pc reply yes-or-no questions. It’s not about guessing numbers like what number of ice lotions you’ll promote (that was Day-2). It’s about guessing “Sure” or “No” — like “Sure, it would rain” or “No, it gained’t.”
Think about you need to know if it would rain at the moment. You have a look at the temperature:
- On a 20°C day, it didn’t rain (No).
- On a 25°C day, it rained (Sure).
- On a 30°C day, it rained (Sure).
You inform the pc these examples, and it learns to guess for a brand new day, like 22°C. Logistic Regression doesn’t give a quantity like “10 ice lotions.” It offers an opportunity — like “70% probability of rain” — after which says “Sure” or “No.”
