時間的には12時間と本来やりたい24時間の半分ですがRMSE 1.6106で
| 2,387R | 1点 | 芝(1,130R) | ダート(1,170R) | 障害(87R) | 8頭以下(121R) | 9～12頭(586R) | 13頭以上(1,680R) | 多点 |
| 単勝 | 20.61% (82.58%) |
20.35% (76.86%) |
21.11% (89.91%) |
17.24% (58.51%) |
30.58% (85.87%) |
22.01% (70.03%) |
19.40% (86.73%) |
48.30% (76.59%) |
| 複勝 | 50.27% (81.72%) |
47.52% (76.51%) |
52.99% (87.10%) |
49.43% (77.01%) |
66.12% (101.40%) |
54.61% (83.62%) |
47.62% (79.64%) |
85.71% (81.67%) |
| 枠連 | 10.41% (71.55%) |
9.32% (58.71%) |
11.73% (86.45%) |
5.13% (18.85%) |
-- (--) |
10.75% (59.98%) |
9.70% (71.54%) |
25.01% (77.98%) |
| 馬連 | 7.79% (67.15%) |
7.08% (61.12%) |
8.55% (72.26%) |
6.90% (76.67%) |
17.36% (96.45%) |
8.87% (67.58%) |
6.73% (64.89%) |
18.43% (64.58%) |
| ワイド | 20.03% (70.86%) |
18.76% (67.65%) |
21.45% (73.41%) |
17.24% (78.28%) |
34.71% (84.46%) |
23.55% (71.59%) |
17.74% (69.63%) |
40.09% (78.79%) |
| 馬単 | 4.15% (65.82%) |
3.98% (65.93%) |
4.44% (65.19%) |
2.30% (72.87%) |
10.74% (117.02%) |
4.44% (53.57%) |
3.57% (66.40%) |
18.43% (62.32%) |
| 三連複 | 4.82% (58.62%) |
4.34% (45.55%) |
5.47% (73.44%) |
2.30% (28.97%) |
13.22% (59.09%) |
4.61% (36.60%) |
4.29% (66.26%) |
11.98% (69.51%) |
| 三連単 | 1.09% (49.29%) |
0.88% (36.97%) |
1.37% (64.85%) |
0.00% (0.00%) |
1.65% (19.59%) |
0.68% (24.22%) |
1.19% (60.18%) |
11.98% (68.50%) |
| 総合 | 51.32% (68.41%) |
48.67% (61.20%) |
54.02% (76.52%) |
49.43% (51.82%) |
67.77% (80.55%) |
55.12% (58.40%) |
48.81% (70.66%) |
86.13% (70.02%) |
目指す精度にはまだまだな感じですorz あまりお薦めではないけど、並行して相対タイム学習を3時間程したのは\(R^{2}\) 0.2107で
| 2,562R | 1点 | 芝(1,253R) | ダート(1,222R) | 障害(87R) | 8頭以下(149R) | 9～12頭(652R) | 13頭以上(1,761R) | 多点 |
| 単勝 | 24.20% (79.70%) |
23.86% (81.72%) |
25.12% (80.77%) |
16.09% (35.75%) |
31.54% (68.39%) |
25.92% (73.25%) |
22.94% (83.05%) |
52.22% (76.43%) |
| 複勝 | 54.45% (82.13%) |
53.55% (83.88%) |
55.32% (80.49%) |
55.17% (79.89%) |
63.09% (89.40%) |
58.74% (81.87%) |
52.13% (81.61%) |
87.94% (81.36%) |
| 枠連 | 12.60% (76.43%) |
11.34% (77.25%) |
13.91% (75.55%) |
10.26% (78.21%) |
-- (--) |
13.34% (74.49%) |
11.58% (72.67%) |
27.53% (73.74%) |
| 馬連 | 8.90% (64.64%) |
8.46% (62.57%) |
9.33% (65.77%) |
9.20% (78.74%) |
18.79% (78.32%) |
11.04% (77.44%) |
7.27% (58.75%) |
21.39% (65.36%) |
| ワイド | 23.15% (82.73%) |
22.27% (82.06%) |
24.30% (84.44%) |
19.54% (68.28%) |
36.91% (81.01%) |
27.61% (79.29%) |
20.33% (84.15%) |
44.30% (78.19%) |
| 馬単 | 4.92% (64.09%) |
4.39% (65.32%) |
5.48% (64.19%) |
4.60% (44.94%) |
10.07% (51.68%) |
6.44% (77.50%) |
3.92% (60.17%) |
21.39% (62.04%) |
| 三連複 | 6.05% (78.77%) |
6.46% (87.04%) |
5.73% (73.89%) |
4.60% (28.28%) |
14.09% (90.74%) |
7.21% (79.36%) |
4.94% (77.54%) |
14.99% (69.95%) |
| 三連単 | 1.05% (65.77%) |
1.12% (69.93%) |
0.98% (63.96%) |
1.15% (31.38%) |
2.68% (58.46%) |
1.84% (72.78%) |
0.62% (63.80%) |
14.99% (64.31%) |
| 総合 | 55.97% (74.26%) |
55.23% (76.20%) |
56.79% (73.62%) |
55.17% (55.39%) |
65.10% (74.00%) |
59.66% (77.00%) |
53.83% (72.72%) |
88.25% (67.71%) |
これはまあ、更に学習時間が短か過ぎたかなぁ。
で、ここで手動でパラメーター設定してLightGBMを試すと
NumberOfLeaves: 79
NumberOfIterations: 1,500
LearningRate: 0.020
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 30
SubsampleFraction: 0.025
FeatureFraction: 0.980
L2Regularization: 0.500
通常用走破タイム学習はRMSE 1.6010で
| 2,387R | 1点 | 芝(1,130R) | ダート(1,170R) | 障害(87R) | 8頭以下(121R) | 9～12頭(586R) | 13頭以上(1,680R) | 多点 |
| 単勝 | 19.02% (72.64%) |
19.03% (84.56%) |
19.57% (63.01%) |
11.49% (47.47%) |
23.14% (65.62%) |
21.84% (74.08%) |
17.74% (72.65%) |
48.14% (76.86%) |
| 複勝 | 48.47% (79.81%) |
46.46% (83.96%) |
50.34% (76.58%) |
49.43% (69.20%) |
61.16% (97.36%) |
53.07% (83.36%) |
45.95% (77.30%) |
85.13% (80.85%) |
| 枠連 | 11.29% (75.40%) |
10.25% (85.83%) |
12.44% (68.62%) |
7.69% (41.92%) |
-- (--) |
12.12% (75.53%) |
10.36% (71.09%) |
26.07% (76.71%) |
| 馬連 | 7.79% (71.94%) |
6.64% (78.00%) |
8.97% (67.42%) |
6.90% (53.91%) |
15.70% (64.63%) |
9.04% (80.02%) |
6.79% (69.64%) |
18.01% (64.85%) |
| ワイド | 20.70% (84.82%) |
18.50% (84.18%) |
22.82% (83.47%) |
20.69% (111.26%) |
33.88% (62.98%) |
23.55% (85.32%) |
18.75% (86.21%) |
39.72% (76.99%) |
| 馬単 | 3.39% (68.24%) |
3.19% (91.26%) |
3.85% (51.09%) |
0.00% (0.00%) |
6.61% (68.68%) |
4.61% (71.28%) |
2.74% (67.15%) |
18.01% (62.55%) |
| 三連複 | 4.78% (82.71%) |
3.45% (104.03%) |
6.07% (65.74%) |
4.60% (34.02%) |
6.61% (33.88%) |
5.29% (53.75%) |
4.46% (96.33%) |
12.69% (60.15%) |
| 三連単 | 0.88% (107.72%) |
0.80% (190.78%) |
1.03% (35.50%) |
0.00% (0.00%) |
1.65% (57.44%) |
1.19% (38.72%) |
0.71% (135.40%) |
12.69% (56.99%) |
| 総合 | 49.85% (80.47%) |
48.05% (100.58%) |
51.62% (63.90%) |
49.43% (44.76%) |
62.81% (64.37%) |
54.61% (70.26%) |
47.26% (84.47%) |
85.55% (63.40%) |
相対タイム学習は\(R^{2}\) 0.2290で
| 2,562R | 1点 | 芝(1,253R) | ダート(1,222R) | 障害(87R) | 8頭以下(149R) | 9～12頭(652R) | 13頭以上(1,761R) | 多点 |
| 単勝 | 24.08% (78.40%) |
24.02% (80.99%) |
24.47% (76.32%) |
19.54% (70.23%) |
34.90% (76.71%) |
25.31% (68.44%) |
22.71% (82.23%) |
53.32% (77.31%) |
| 複勝 | 55.43% (81.24%) |
53.79% (80.37%) |
56.79% (81.72%) |
59.77% (87.13%) |
64.43% (86.04%) |
59.82% (81.52%) |
53.04% (80.73%) |
88.41% (81.41%) |
| 枠連 | 12.38% (64.41%) |
11.62% (60.44%) |
13.05% (67.97%) |
12.82% (65.51%) |
-- (--) |
13.96% (69.68%) |
11.07% (58.69%) |
28.74% (79.15%) |
| 馬連 | 9.29% (60.80%) |
8.86% (56.62%) |
9.82% (65.88%) |
8.05% (49.77%) |
19.46% (92.75%) |
11.66% (70.84%) |
7.55% (54.38%) |
22.29% (69.34%) |
| ワイド | 22.87% (70.94%) |
21.23% (62.50%) |
24.63% (79.10%) |
21.84% (77.82%) |
37.58% (70.07%) |
27.30% (76.32%) |
19.99% (69.02%) |
45.36% (76.88%) |
| 馬単 | 5.62% (64.47%) |
5.35% (64.54%) |
5.89% (65.22%) |
5.75% (52.99%) |
12.08% (92.48%) |
7.21% (65.09%) |
4.49% (61.87%) |
22.29% (68.06%) |
| 三連複 | 6.21% (67.11%) |
5.91% (62.48%) |
6.55% (73.59%) |
5.75% (42.64%) |
12.75% (62.82%) |
6.75% (56.66%) |
5.45% (71.34%) |
15.42% (71.89%) |
| 三連単 | 1.56% (61.32%) |
1.28% (52.16%) |
1.80% (69.19%) |
2.30% (82.76%) |
2.68% (84.77%) |
2.61% (98.70%) |
1.08% (45.50%) |
15.42% (65.67%) |
| 総合 | 56.67% (68.64%) |
55.23% (65.10%) |
57.77% (72.40%) |
62.07% (66.11%) |
66.44% (80.81%) |
60.74% (73.40%) |
54.34% (65.47%) |
88.68% (69.80%) |
どちらも10分程度で学習した結果なので、AutoMLで長時間するのが良いのか、パラメーターを探り当てる方が良いのかだなぁ。この辺りの機能追加で最近時間取られてたので、もう少しモデル探しを頑張るか。