source: orange/Orange/testing/regression/results/orange/modules/tuning1.py.txt @ 9679:3879dea56188

Revision 9679:3879dea56188, 4.7 KB checked in by Miha Stajdohar <miha.stajdohar@…>, 2 years ago (diff)

Moved and renamed testing.

Line 
1*** optimization  1: [0.97058829757682885]:
2*** optimization  2: [0.97642948164590782]:
3*** optimization  3: [0.98338801755375926]:
4*** optimization  4: [0.98788177744892502]:
5*** optimization  5: [0.98894238973780235]:
6*** optimization  10: [0.98692031923626455]:
7*** optimization  15: [0.98842239138206578]:
8*** optimization  20: [0.97804067310171638]:
9*** Optimal parameter: minSubset = 5
10Optimal setting:  5
11*** optimization  1: [0.98321908602150532]:
12*** optimization  2: [0.97819892473118264]:
13*** optimization  3: [0.9912679211469535]:
14*** optimization  4: [0.9937656810035842]:
15*** optimization  5: [0.99075044802867385]:
16*** optimization  10: [0.9872647849462366]:
17*** optimization  15: [0.98976926523297493]:
18*** optimization  20: [0.99105062724014337]:
19*** Optimal parameter: minSubset = 4
20*** optimization  1: [0.97296370967741941]:
21*** optimization  2: [0.97278673835125462]:
22*** optimization  3: [0.98086245519713267]:
23*** optimization  4: [0.98209901433691749]:
24*** optimization  5: [0.98543682795698928]:
25*** optimization  10: [0.98856854838709673]:
26*** optimization  15: [0.99162634408602157]:
27*** optimization  20: [0.98600806451612899]:
28*** Optimal parameter: minSubset = 15
29*** optimization  1: [0.98023073476702494]:
30*** optimization  2: [0.98306899641577061]:
31*** optimization  3: [0.98245295698924728]:
32*** optimization  4: [0.9896012544802868]:
33*** optimization  5: [0.98472670250896055]:
34*** optimization  10: [0.98965277777777783]:
35*** optimization  15: [0.98743503584229386]:
36*** optimization  20: [0.97437948028673826]:
37*** Optimal parameter: minSubset = 10
38*** optimization  1: [0.96825044802867388]:
39*** optimization  2: [0.97539202508960576]:
40*** optimization  3: [0.97483422939068098]:
41*** optimization  4: [0.98026657706093201]:
42*** optimization  5: [0.97956765232974918]:
43*** optimization  10: [0.9769332437275986]:
44*** optimization  15: [0.97734543010752684]:
45*** optimization  20: [0.9740815412186381]:
46*** Optimal parameter: minSubset = 4
47*** optimization  1: [0.96364247311827955]:
48*** optimization  2: [0.974209229390681]:
49*** optimization  3: [0.97841621863799277]:
50*** optimization  4: [0.98721102150537632]:
51*** optimization  5: [0.98688396057347672]:
52*** optimization  10: [0.98780689964157697]:
53*** optimization  15: [0.98020833333333335]:
54*** optimization  20: [0.97671370967741944]:
55*** Optimal parameter: minSubset = 10
56*** optimization  1: [0.97435707885304657]:
57*** optimization  2: [0.97433705471435883]:
58*** optimization  3: [0.97782107197717805]:
59*** optimization  4: [0.97559295223465736]:
60*** optimization  5: [0.98345644978421476]:
61*** optimization  10: [0.98451827774120404]:
62*** optimization  15: [0.98160682283666145]:
63*** optimization  20: [0.9802781892326824]:
64*** Optimal parameter: minSubset = 10
65*** optimization  1: [0.9764701740911419]:
66*** optimization  2: [0.98519868700168234]:
67*** optimization  3: [0.98775030173359668]:
68*** optimization  4: [0.98942574610489364]:
69*** optimization  5: [0.98909228476336764]:
70*** optimization  10: [0.98262768817204293]:
71*** optimization  15: [0.98151337685611872]:
72*** optimization  20: [0.98251312083973374]:
73*** Optimal parameter: minSubset = 4
74*** optimization  1: [0.98090168056469895]:
75*** optimization  2: [0.99166556945358786]:
76*** optimization  3: [0.98834444261575594]:
77*** optimization  4: [0.98837159863945589]:
78*** optimization  5: [0.99087895911052581]:
79*** optimization  10: [0.98963769109794453]:
80*** optimization  15: [0.98902969790066564]:
81*** optimization  20: [0.9841350760734402]:
82*** Optimal parameter: minSubset = 2
83*** optimization  1: [0.97177195340501799]:
84*** optimization  2: [0.97940412186379944]:
85*** optimization  3: [0.98024641577060934]:
86*** optimization  4: [0.98027105734767028]:
87*** optimization  5: [0.98310707885304649]:
88*** optimization  10: [0.98413978494623655]:
89*** optimization  15: [0.98651209677419371]:
90*** optimization  20: [0.97995519713261658]:
91*** Optimal parameter: minSubset = 15
92*** optimization  1: [0.97084229390681009]:
93*** optimization  2: [0.97555107526881724]:
94*** optimization  3: [0.98384184587813628]:
95*** optimization  4: [0.97722222222222244]:
96*** optimization  5: [0.98428539426523298]:
97*** optimization  10: [0.989247311827957]:
98*** optimization  15: [0.987780017921147]:
99*** optimization  20: [0.98074148745519696]:
100*** Optimal parameter: minSubset = 10
101Untuned tree: 0.925
102Tuned tree: 0.983
103*** optimization  1: [0.97058829757682885]:
104*** optimization  2: [0.97642948164590782]:
105*** optimization  3: [0.98338801755375926]:
106*** optimization  4: [0.98788177744892502]:
107*** optimization  5: [0.98894238973780235]:
108*** optimization  10: [0.98692031923626455]:
109*** optimization  15: [0.98842239138206578]:
110*** optimization  20: [0.97804067310171638]:
111*** Optimal parameter: ['split.continuousSplitConstructor.minSubset', 'split.discreteSplitConstructor.minSubset'] = 5
112Optimal setting:  5.0
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