Results in SVMs

The SVM was tested upon three separate algorithms.

1. Linear
2. Polynomial
3. RBF
4. Sigmoid

Different SVM kernels for IRIS dataset - Source: scikit-learn docs

Linear Kernel

	Precision	Recall	F1-Score	Support
0.0	1.0	1.0	1.0	12
1.0	1.0	1.0	1.0	11
2.0	1.0	1.0	1.0	18
Accuracy			1.0	41
Macro Avg	1.0	1.0	1.0	41
Weighted Avg	1.0	1.0	1.0	41

Polynomial Kernel

	Precision	Recall	F1-Score	Support
0.0	1.0	1.0	1.0	12
1.0	1.0	0.55	0.71	11
2.0	0.78	1.0	0.88	18
Accuracy			0.88	41
Macro Avg	0.93	0.85	0.86	41
Weighted Avg	0.90	0.88	0.87	41

RBF Kernel

	Precision	Recall	F1-Score	Support
0.0	1.0	1.0	1.0	12
1.0	1.0	1.0	1.0	11
2.0	1.0	1.0	1.0	18
Accuracy			1.0	41
Macro Avg	1.0	1.0	1.0	41
Weighted Avg	1.0	1.0	1.0	41

Sigmoid Kernel

	Precision	Recall	F1-Score	Support
0.0	1.0	1.0	1.0	12
1.0	1.0	0.45	0.62	11
2.0	0.78	0.78	0.88	18
3.0	0.0	0.0	0.0	0
Accuracy			0.76	41
Macro Avg	0.75	0.56	0.62	41
Weighted Avg	1.00	0.76	0.84	41