1. A. Given the data set below, apply the k-Nearest Neighbor algorithm to classify the test data for k=1 and k=3. Use the Euclidean distance metric.

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

B. Compute the confusion matrix, accuracy, precision, recall, and F1 measures given your answers to problem 1.

C. Assume you have the data set given below, which provides hypothetical examples of instances when people did or did not get hired for a job. It consists of three categorical attributes and a label that indicates "hired" or "not hired". Using this data, induce a decision tree using information gain for splitting the nodes, showing the calculations at each step.

Training Set
#	Experience (EXP)	Sufficient Qualifications? (QUAL)	Opinions of References (REFOP)	true label
1	good	Yes	favorable	1
2	excellent	Yes	favorable	1
3	none	No	favorable	0
4	good	No	not favorable	0
5	good	Yes	not favorable	0
6	excellent	Yes	not favorable	0
7	excellent	Yes	favorable	1
8	good	Yes	favorable	1
9	none	Yes	favorable	1
10	none	Yes	not favorable	0