Table 4.

Performance comparison from various algorithms.

Precision		Recall	F1-score	Support
Decision tree
0	0.38	0.50	0.43	6
1	0.88	0.81	0.85	27
Accuracy			0.76	0.33
Macro avg	0.63	0.66	0.64	33
Weighted avg	0.79	0.76	0.77	33
[[0.09 0.09]
[0.15 0.67]]
Random forest
0	0.00	0.00	0.00	6.
1	0.82	1.00	0.90	27
Accuracy			0.82	33
Macro avg	0.41	0.50	0.45	33
Weighted avg	0.67	0.82	0.74	33
[[0. 0.18]
[0. 0.82]]
Gaussian naive bayes
0	1.00	0.17	0.29	6
1	0.84	1.00	0.92	27
Accuracy			0.85	33
Macro avg	0.92	0.58	0.6	33
Weighted avg	0.87	0.85	0.80	33
[[0.03 0.15]
[0. 0.82]]
Gradient descent (logistic)
0	0.62	0.83	0.71	3
1	0.96	0.89	0.92	27
Accuracy			0.88	33
Macro avg	0.79	0.86	0.82	33
Weighted avg	0.90	0.88	0.89	33
[[0.15 0.03]
[0.09 0.73]]
Gradient descent (hinge)
0	0.00	0.00	0.00	6
1	0.82	1.00	0.90	27
Accuracy			0.82	33
Macro avg	0.41	0.50	0.45	33
Weighted avg	0.67	0.82	0.74	23
[[0. 0.18]
[0. 0.82]]
Support vector machines
0	0.80	0.67	0.73	6
1	0.93	0.96	0.95	27
Accuracy			0.91	33
Macro avg	0.86	0.81	0.84	33
Weighted avg	0.91	0.91	0.91	33
[[0.12 0.06]
[0.03 0.79]]
MLP (Adam)
0	0.18	1.00	0.31	6
1	0.00	0.00	0.00	27
Accuracy			0.18	33
Macro avg	0.09	0.50	0.15	33
Weighted avg	0.03	0.18	0.06	333
[[0.18 0. ]
[0.82 0. ]]
MLP (LBFGS)
0	0.00	0.00	0.00	6
1	0.82	1.00	0.90	27
Accuracy			0.82	33
Macro avg	0.41	0.50	0.45	33
Weighted avg	0.67	0.82	0.74	33
[[0. 0.18]
[0. 0.82]]