Linear regression middleschool

1. AI Club

2. AI Club
Linear Regression

3. Regression Basics
•Type of “Supervised Learning” that returns numbers (not
categories)
•Needs a label (right answer) for every example
•Can take any type of Feature (numbers, categories, text etc.)
• Common real life examples take 100s of features!

4. Linear Regression
0
6
1
3
0.5
Feature
Label
Features
0.2 2.8
0.5 3.6
0.7 4.5
1.0 5.5
What is the general trend of the data in the
graph?
Think about a model that captures this trend.
Label

5. Linear Regression
0
6
1
3
0.5
Feature
Label
Label
Features
0.2 2.8
0.5 3.6
0.7 4.5
1.0 5.5

6. Linear Regression
0
6
1
0.5
Feature
• New Feature value: 0.8
• Predict Label
Label
Features
0.2 2.8
0.5 3.6
0.7 4.5
1.0 5.5
Label
3
Guess the prediction, when
feature = 0.8

7. Linear Regression
y
0
6
1
3
0.5 0.8
Feature
• New Feature value: 0.8
• Predict Label
Label
Features
0.2 2.8
0.5 3.6
0.7 4.5
1.0 5.5

8. Linear Regression
y
0
6
1
3
0.5 0.8
5
Feature
• New Feature value: 0.8
• Predict Label
Label
Features
0.2 2.8
0.5 3.6
0.7 4.5
1.0 5.5

9. Linear Regression - line equation
y
0
6
1
3
0.5 0.8
5
Feature
• New Feature value: 0.8
• Predict Label
y = w0 + w1 x Feature
Label
Features
0.2 2.8
0.5 3.6
0.7 4.5
1.0 5.5

10. Linear Regression - another example
Square Feet Price (k$)
1250 500
3200 2200
950 350
2100 950
Label
Feature
500 1000 1500 2000
500
2500
1500
1000
2000
3000
Square Feet (Feature)
Price
(k$)
(Label)
Price = w0 + w1 x Square Feet

11. Linear Regression - another example
Average = w0 + w1 x A + w2 x B + w3 x C + w4 x D

12. Linear Regression - Averages
• Averages formula?

13. Linear Regression - Averages
• AVERAGE = (A + B + C + D)/4

14. Linear Regression - Averages
• AVERAGE = (A + B + C + D)/4
• How is the above formula related to linear regression model?
Average = w0 + w1 x A + w2 x B + w3 x C + w4 x D

15. Linear Regression - Averages
• AVERAGE = (A + B + C + D)/4 = (A/4) + (B/4) + (C/4) + (D/4)
• How is the above formula related to linear regression model?
Average = w0 + w1 x A + w2 x B + w3 x C + w4 x D

16. Linear Regression - Averages
• AVERAGE = (A + B + C + D)/4 = (A/4) + (B/4) + (C/4) + (D/4)
= (1/4)A + (1/4)B + (1/4)C + (1/4)D
• How is the above formula related to linear regression model?
Average = w0 + w1 x A + w2 x B + w3 x C + w4 x D

17. THANK YOU
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