Little Bits of Artificial Intelligence

1. What is Logistic Regression?

Imagine we’re a gatekeeper at a club. We have a list of rules that help us decide whether to let a person in or not. These rules are based on things like:

• Their age
• Whether they’re dressed formally
• Whether they have a membership card

We don’t need to assign a score like in a school exam — we just need to answer:

Yes, let them in
No, don’t let them in

This is a binary decision: Yes or No, 1 or 0, True or False.

Now, over time, we’ve observed thousands of people and whether they were allowed in or not. We learn patterns:

• Most people above 21 with a membership card are let in
• Most people below 18 are no
• Some edge cases exist, but you spot a curve in the pattern

Logistic Regression is like we are learning from all that data, and coming up with a smart rule — a formula — to predict: “Given this new person’s details, should I let them in?”

Unlike Linear Regression (which predicts numbers), Logistic Regression predicts probabilities that something belongs to Class A (0) or Class B (1).

2. Real-World Use Cases

#1: Email Spam Detection

We receive emails every day. Some are spam, some are not.

Let’s say we want to train a computer to detect spam emails.

We’ll look at features like:

• Does it contain the word “win”?
• Is there a suspicious link?
• Is the sender known?
• Does it have attachments?

Logistic Regression takes these inputs and learns from thousands of example emails. It then learns to predict:

“What’s the probability this email is spam?”

If the probability is above a threshold (say 0.8 or 80%), it marks it as spam.

#2: Health Risk Prediction

A doctor wants to predict whether a patient has diabetes based on:

• Age
• Body Mass Index (BMI)
• Blood pressure
• Family history

The doctor can access past data: patients with these traits and whether they developed diabetes (Yes = 1, No = 0).

Logistic Regression helps by modeling the probability:

“How likely is this patient to have diabetes?”

If it’s a high probability (say 95%), then the doctor can plan early intervention.