Apply the k-Nearest Neighbor algorithm to classify the test data for k=1 and k=3
ID: 3903759 • Letter: A
Question
Apply the k-Nearest Neighbor algorithm to classify the test data for k=1 and k=3 using the Euclidean distance metric. Show the resulting distance matrix used for the classification. Use Excel or Python please.
Training Set
#
x1
x2
true label
1
0.453705
-0.0106
1
2
3.258589
0.169734
1
3
3.184656
-0.83691
0
4
-0.42561
1.385033
0
5
0.658765
-1.87715
0
6
-0.40507
-1.9574
0
7
-4.52775
4.123102
1
8
2.538689
-1.5386
1
9
-1.04649
-3.59664
1
10
2.967113
0.505111
0
Testing Set
#
x1
x2
true label
predicted label
11
-4.69237
-4.77898
1
12
-2.1147
-1.81277
0
13
4.277164
-4.83136
1
14
-1.33862
-0.93995
0
15
-4.02728
-4.96129
1
16
4.968125
3.757161
1
17
-2.19987
-3.48712
0
18
2.849136
-3.33965
0
19
-4.30273
2.530094
1
20
4.690116
-0.36379
1
Training Set
#
x1
x2
true label
1
0.453705
-0.0106
1
2
3.258589
0.169734
1
3
3.184656
-0.83691
0
4
-0.42561
1.385033
0
5
0.658765
-1.87715
0
6
-0.40507
-1.9574
0
7
-4.52775
4.123102
1
8
2.538689
-1.5386
1
9
-1.04649
-3.59664
1
10
2.967113
0.505111
0
Explanation / Answer
Hello Student!
Here goes the code :
***** Code ends here ****
Output for k = 3
Predicted value =1, Actual value =1
Predicted value =1, Actual value =0
Predicted value =1, Actual value =1
Predicted value =1, Actual value =0
Predicted value =1, Actual value =1
Predicted value =1, Actual value =1
Predicted value =1, Actual value =0
Predicted value =1, Actual value =0
Predicted value =0, Actual value =1
Predicted value =1, Actual value =1
Accuracy: 50.0%
Output for k = 1
Predicted value =1, Actual value =1
Predicted value =0, Actual value =0
Predicted value =1, Actual value =1
Predicted value =0, Actual value =0
Predicted value =1, Actual value =1
Predicted value =0, Actual value =1
Predicted value =1, Actual value =0
Predicted value =1, Actual value =0
Predicted value =1, Actual value =1
Predicted value =1, Actual value =1
Efficiency: 70.0%
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