またもAIに踊らされてる?(笑) AutoMLやModel Builderの限界?に達したかは別として、精度アップに苦労してるので別のアプローチに挑みました。またPython絡みです。まあ、現在のデータサイエンスの中核なのは間違いないのかもですが、AutoMLやModel Builderとは違うアプローチで最適なLightGBMのパラメータを探すもの? Optunaとかいうものを試し、そのパラメータ使ってLightGBMのモデルを作成して検証してみると
| 3,455R | 1点 | 芝(1,689R) | ダート(1,646R) | 障害(120R) | 8頭以下(219R) | 9～12頭(809R) | 13頭以上(2,427R) | 多点 |
| 単勝 | 24.98% (74.49%) |
23.74% (70.17%) |
26.37% (79.27%) |
23.33% (69.67%) |
35.16% (84.38%) |
26.82% (75.62%) |
23.44% (73.21%) |
54.70% (76.27%) |
| 複勝 | 56.93% (82.66%) |
55.77% (81.05%) |
57.59% (83.87%) |
64.17% (88.58%) |
67.58% (84.84%) |
61.93% (82.29%) |
54.31% (82.58%) |
88.08% (81.13%) |
| 枠連 | 13.53% (70.96%) |
12.56% (68.74%) |
13.66% (70.17%) |
25.25% (115.25%) |
-- (--) |
16.07% (74.36%) |
11.83% (65.32%) |
29.04% (75.23%) |
| 馬連 | 10.33% (62.09%) |
9.71% (54.96%) |
10.45% (67.31%) |
17.50% (90.83%) |
23.29% (70.32%) |
12.73% (68.03%) |
8.36% (59.37%) |
23.53% (71.11%) |
| ワイド | 24.31% (75.44%) |
23.68% (68.80%) |
24.61% (82.36%) |
29.17% (73.83%) |
47.03% (85.39%) |
29.05% (77.35%) |
20.68% (73.90%) |
45.82% (76.78%) |
| 馬単 | 5.50% (58.72%) |
4.97% (48.41%) |
5.83% (67.15%) |
8.33% (88.33%) |
14.16% (88.58%) |
6.18% (55.55%) |
4.49% (57.09%) |
23.53% (69.60%) |
| 三連複 | 6.14% (85.96%) |
6.63% (61.22%) |
5.65% (113.10%) |
5.83% (61.83%) |
15.07% (61.42%) |
9.02% (86.51%) |
4.37% (87.99%) |
16.03% (68.44%) |
| 三連単 | 1.48% (48.13%) |
1.78% (48.33%) |
1.22% (49.78%) |
0.83% (22.67%) |
5.02% (85.89%) |
2.22% (57.97%) |
0.91% (41.44%) |
16.03% (65.94%) |
| 総合 | 58.35% (69.79%) |
57.31% (62.58%) |
58.75% (76.66%) |
67.50% (75.51%) |
73.52% (80.12%) |
63.04% (72.21%) |
55.42% (67.61%) |
88.39% (69.65%) |
こんな感じでした。これRMSEとか\(R^{2}\)値とか不明なんだけど^^; ざっくりとした話、50回が1時間程度で終わって得たパラメータ使ってモデル作成自体は1分程度。次は200回または500回。200回で4時間程度、500回は10時間程度なんだが、ダービーデイにこんな事してるとはorz
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