Simple Explanations for Linear and Logistic Regression

Let's say you want to know how tall a plant will be based on how much water you give it. You start by measuring the height of the plant when it has 0 water, and then you give it a little bit of water and measure its height again. You do this again and again, giving it different amounts of water each time and measuring its height each time.

When you have all the measurements, you can draw a line that goes through all the points on a graph. This line is called a "regression line" and it helps you predict how tall the plant will be if you give it a certain amount of water.

This is called "linear regression" because the line is straight and it helps you see the relationship between the two things you're measuring - in this case, water and height.

In the same way, you can use linear regression to predict other things, like how fast a car will go based on how much gas is in the tank, or how much money you'll make based on how many hours you work.

So, linear regression is just a way to predict one thing based on another thing, by drawing a straight line through all the measurements you've taken.

Okay, let me try to explain logistic regression in simple terms as well.

Imagine you have a bunch of apples and you want to sort them into two groups: ripe apples and unripe apples. You know that ripe apples are usually rounder and have a reddish color, while unripe apples are usually greener and harder.

To sort the apples, you can use logistic regression. With logistic regression, you're trying to find a line that separates the ripe apples from the unripe apples. This line is called a "decision boundary".

You start by measuring the characteristics of each apple - its roundness, color, and hardness. You use these measurements to create a mathematical formula that predicts whether an apple is ripe or unripe based on its characteristics.

The formula gives you a number between 0 and 1, which tells you the probability that an apple is ripe. For example, if the formula gives you a value of 0.8, that means there's an 80% chance the apple is ripe.

The decision boundary is the line where the formula gives you a value of exactly 0.5 - that's the point where the model can't decide if the apple is ripe or unripe. Any apple above the line is predicted to be ripe, and any apple below the line is predicted to be unripe.

Logistic regression is useful when you're trying to predict a binary outcome - that is, an outcome that can only have two possible values, like "ripe" or "unripe", "yes" or "no", or "pass" or "fail". It's commonly used in fields like medicine, where doctors might use logistic regression to predict whether a patient has a certain disease based on their symptoms and test results.