Question

In Matlab

1. Partition the fisheriris dataset into a 60% training partition and 40% test partition

a. The fisheriris dataset comes standard with matlab and can be loaded into the matlab
environment by typing 'load fisheriris'.

b. Use only the first 100 entries (the first 2 classes) for the remaining part of the
assignment:

i. species(l:100)

ii. meas(l:100,:)

2. Generate a confusion matrix for the test partition using each the classifiers from the previous
labs.

a. Bayesian classifier

b. Nearest Neighbor (k=l)

Answer 1

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ANSWER::-

AS FOR GIVEN DATA....

1. Partition the fisheriris dataset into a 60% training partition and 40% test partition

a. The fisheriris dataset comes standard with matlab and can be loaded into the matlab
environment by typing 'load fisheriris'.

b. Use only the first 100 entries (the first 2 classes) for the remaining part of the
assignment:

i. species(l:100)

ii. meas(l:100,:)

2. Generate a confusion matrix for the test partition using each the classifiers from the previous
labs.

a. Bayesian classifier

b. Nearest Neighbor (k=l)

SOL ::-

(1 a). The fisheriris dataset comes standard with matlab and can be loaded into the matlab

1. a. >>load fisheriris

>>tr=60;

>>te=40;

(b). Use only the first 100 entries (the first 2 classes) for the remaining part of the
assignment:

i. species(l:100)

ii. meas(l:100,:)

1. b. >>s=species(1:100);

>>m=meas(1:100,:);

2. Generate a confusion matrix for the test partition using each the classifiers from the previous
labs.

(2)

(a). Bayesian classifier

. a. >>out1=fitcnb(m,s);

>>c1=out1.predict(m);

>>cmat1=confusionmat(s,c1);

(b.) Nearest Neighbor (k=l)

2. b. >>out2=fitcknn(m,s);

>>c2=out2.predict(m);

>>cmat2=confusionmat(s,c2);

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