Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Table 5

Evolution of optimization procedure for XGB (first 50 iterations only).

Iter	Target	Colsample_bytree	Gamma	Learning_rate	Max_depth	Min_child_weight	Reg_alpha	Subsample
1	−7.056	0.8801	0.8997	0.2827	20.34	4.861	0.006904	0.5405
2	−6.516	0.5613	0.4843	0.09498	6.732	5.114	0.007405	0.9069
3	−16.69	0.5848	0.2136	0.1965	19.36	9.066	0.01777	0.6896
4	−8.5	0.9966	0.8161	0.1771	5.423	7.812	0.008559	0.9149
5	−8.822	0.7103	0.598	0.1231	6.284	6.038	0.007801	0.9096
6	−5.852	0.5	0.3518	0.06219	7.254	4.037	0.006944	0.9037
7	−9.343	0.5	0.5106	0.01885	5.633	3.34	0.005851	0.8316
8	−6.15	0.5	0.2702	0.1017	8.293	4.807	0.007843	0.9567
9	−7.601	1	1	0.3	7.727	4.384	0.02	0.5
10	−6.5	1	1	0.3	20.87	2.587	0.005	0.5
11	−6.26	1	1	0.3	22.83	3.751	0.005	0.5
12	−25.55	0.5	0	0.001	23.13	1.845	0.02	1
13	−7.601	1	1	0.3	22.05	4.527	0.005	0.5
14	−6.26	1	1	0.3	19.62	3.248	0.005	0.5
15	−7.601	1	1	0.3	23.95	4.842	0.005	0.5
16	−25.48	0.5	0	0.001	8.813	3.053	0.005	1
17	−5.912	0.5	0.1875	0.2799	7.554	4.955	0.008345	1
18	−25.44	0.5	0.8634	0.001	8.342	5.686	0.005	1
19	−6.418	0.5345	0.2921	0.2898	8.177	4.709	0.01122	0.9844
20	−11.41	0.6047	0.03536	0.01905	7.271	4.588	0.01701	0.6337
21	−5.658	0.5	0.73	0.3	7.259	4.412	0.005	1
22	−6.839	0.9758	0.9844	0.2983	20.45	3.433	0.005328	0.5055
23	−25.48	0.531	0.8686	0.001	6.918	3.876	0.005	1
24	−6.541	0.5	0.4384	0.2496	7.453	4.4	0.00543	0.9837
25	−16.64	0.8722	0.1358	0.05604	13.76	9.028	0.01675	0.5171
26	−5.996	0.6539	0.2812	0.1042	7.178	4.032	0.01673	0.919
27	−6.889	0.7965	0.4768	0.2854	7.075	5.049	0.01465	0.9937
28	−5.911	0.8249	0.2209	0.1936	24.47	1.008	0.01477	0.6331
29	−16.66	0.6999	0.2054	0.2231	16.34	7.16	0.01825	0.5339
30	−6.5	1	1	0.3	20.18	2.931	0.005	0.5
31	−6.559	0.5	0.7239	0.3	7.301	4.792	0.005	0.9979
32	−6.974	0.5	0.08312	0.23	7.39	3.94	0.005782	0.9513
33	−6.219	0.7449	0.6745	0.2195	19.98	3.314	0.01041	0.6499
34	−6.614	0.8682	0.3464	0.03563	14.74	2.282	0.006153	0.8187
35	−6.252	0.8297	0.9535	0.2535	14.33	3.153	0.007105	0.6695
36	−11.64	0.6522	0.5504	0.02992	13.11	6.753	0.01481	0.7805
37	−11.8	0.8327	0.5598	0.02797	15.28	5.888	0.009776	0.6274
38	−6.562	0.7739	0.2751	0.293	17.17	4.494	0.01663	0.8818
39	−6.339	0.8632	0.9508	0.1429	21.26	1.244	0.01272	0.9479
40	−5.643	0.8481	0.6627	0.1492	14.53	2.736	0.006655	0.741
41	−5.792	0.5397	0.2942	0.09671	7.252	4.123	0.01874	0.8521
42	−5.838	0.8593	0.7312	0.1051	14.02	2.605	0.005175	0.7117
43	−6.016	0.6624	0.2504	0.1153	8.384	3.601	0.00867	0.7995
44	−6.313	0.8624	0.2579	0.2642	13.42	1.179	0.01796	0.9567
45	−5.629	0.7182	0.3777	0.122	8.18	3.918	0.009016	0.7433
46	−5.981	0.7067	0.9652	0.3	14.33	2.576	0.02	0.9897
47	−9.125	0.8218	0.602	0.2969	18.08	6.542	0.0195	0.777
48	−6.014	0.9996	0.9764	0.3	14.38	2.526	0.02	0.5146
49	−16.74	0.7799	0.6707	0.007666	25.28	1.548	0.007938	0.7785
50	−6.792	0.5	0.7475	0.3	14.31	2.629	0.02	0.5203