Little Bits of Artificial Intelligence

1. The Story: Predicting House Prices — The Realtor’s Dilemma

Characters:

• Reena, a real estate agent
• The House Data: Size, Age, Distance from school, Crime Rate, etc.
• The Goal: Reena wants to predict house prices based on past data.

2. Step-by-Step Explanation of Ridge Regression (with Descriptive Visualization)

Step 1: Reena collects data

She gathers:

• 50 houses
• For each: square footage, age, crime rate, school distance, and actual price

She puts them into a table:

Why?
She wants to find patterns. Bigger houses usually cost more — but some data points don’t follow the trend due to location or crime rate.

Step 2: She tries to fit a line to predict price

She uses basic Linear Regression. It tries to find the best equation:

Price = w1×Size+w2×Age+…+Bias

Why? She believes a formula will help her estimate prices for new houses in future.

Problem: Overfitting

Reena adds 20+ features like:

• Nearby restaurants, traffic data, number of windows, color of walls

Now, her formula becomes super-complicated:

Price = 0.82×Size+1.4×Age−12.8×Crime+…

Why is this bad?

• It fits the past data perfectly (even noise).
• But fails terribly when used on new houses — overfit!

Step 3: Reena adds a Rule — Don’t trust crazy numbers!

She says:“I want to make accurate predictions, but I don’t want my formula to go crazy with huge numbers just to fit the past.”

So, she adds a penalty for large coefficients — like saying:

“Hey model, if you’re going to use ‘crime rate’ as a feature, fine — but don’t make it dominate everything unless it’s really important!” This is Ridge Regression.

Step 4: Reena balances two things

1. Fitting the data → Get predictions close to actual house prices (like linear regression)

2. Keeping the formula simple → Don’t allow very large weights (penalize big numbers)

This new goal becomes:

Final Score=Error+λ×(Penalty for large numbers)

Why? She wants to find a middle ground — accurate yet stable predictions.

Step 5: What does lambda (λ) mean in Reena’s world?

• λ = 0 → “Fit the data as tightly as you want” (may overfit)
• λ = 10 → “Be careful — don’t stretch too much to fit outliers”
• λ = 100 → “Stick to the middle — don’t trust noisy data”

Why is this useful?

Reena now builds a model that works well for new houses — it’s not too clever, not too dumb — just right.

3. Summary of Learning Ridge Regression