Knn intro advanced_middleschool

1. AI Club

2. AI Club
KNN
K-Nearest Neighbours Algorithm

3. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
Adult
Child

4. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
Adult
Child

5. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
Adult
Child
Input to AI
Years in school = 6
Height = 5ft

6. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
If K = 1
Adult
Child

7. Classification Dataset
Years in
school
height label
0 2.18 child
7 5.58 adult
4 5.95 adult
4 4.36 child
2 5.53 adult
9 5.38 child
11 5.29 adult
4 5.91 adult
0 3.96 child
1 2.72 child
0 12
7
6
3
Years in school
Height
(ft)
If K = 3
Adult
Child

8. Regression Dataset
index sq_ft age price
1 895 26 707158
2 827 61 285327
3 873 58 365744
4 847 58 338163
5 801 66 204300
6 522 58 14117
7 784 46 388363
8 450 25 121220
9 657 33 381713
10 425 52 82357
0
70
450
30
Sq_ft
age
900
1
3
2
4
5
6
7
8
9
10

9. Regression Dataset
index sq_ft age price
1 895 26 707158
2 827 61 285327
3 873 58 365744
4 847 58 338163
5 801 66 204300
6 522 58 14117
7 784 46 388363
8 450 25 121220
9 657 33 381713
10 425 52 82357
0
70
450
30
Sq_ft
age
900
1
3
2
4
5
6
7
8
9
10
If K = 3

10. How do you find the best K?
• The process of find the best hyper-parameters
for a algorithm is called hyper-parameter
tuning.
• In case of KNN, we tune a single parameter
called K.

11. What is a good price for
House 1?
(a)10,000 (b) 5,000?
House
Number
Square feet age Price
1 1000 0
2 800 0 10,000$
3 1000 100 5,000$

12. What do you think KNN will do if K=1?
Who is the nearest neighbor of the first
house?
House
Number
Square feet age Price
1 1000 0
2 800 0 10,000$
3 1000 100 5,000$

13. Square feet age Price
1000 0
800 0 10,000$
1000 100 5,000$
Distance(House 1, House 2) = 200
Distance(House 1, House 3) = 100

14. Drawbacks of KNN
• Scaling: Different features may have different
ranges in numbers

15. Lets try it!
• Build an AI to predict
house prices
• Change K and see
how the AI behaves!

16. THANK YOU
https://aiclub.world
info@pyxeda.ai