2026/10/18

第31回秋華賞

ちょっとカーオーディオの取付で時間が取れず、ざっくりと独自予想してみた。

土つかずの1番人気は強そうだけど、距離伸びて飛んでもらい2,000mの実績で狙ってみました。

さて、カーオーディオの作業に戻ります。

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Pioneer MVH-5600

ポンコツマイカーのナビが逝きました。まあね、HDDナビなので20年も動く事が奇跡レベルなので諦めました。ポイント使ってPioneer MVH-5600を購入。DIYでって初めてやったんですが、ステーが無い😭 これ、近所のイエローハットとかに行くとあるんだろうか? 元々2DINのストラーダが付いてたので1DINでは下が空くのは分かってたのでAD-378って小物入れも一緒に購入して有ったんだが、ステーが無いとは想定してなかった。まあ、そこは素人だね(笑)

午前中の到着して作業したんだがってか、既に昨日ナビは外しておいた。ハーネス関連は流用出来たっぽいけど、多分、配線が完全じゃないのでなのか音が出てない。やり直さなきゃです。

Pioneer MVH-5600 12,164円
AD-378 1,082円

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ランキング学習のメモ

先日のリリースに添付したランキング学習のモデルはRankCK3000_loop1_NDCG7679_7741.zipで

2,610R 1点 芝(1,278R) ダート(1,243R) 障害(89R) 8頭以下(157R) 9～12頭(665R) 13頭以上(1,788R) 多点
単勝 24.75%
(75.89%)		 25.04%
(81.96%)		 24.70%
(71.30%)		 21.35%
(52.81%)		 33.76%
(74.78%)		 27.07%
(71.82%)		 23.10%
(77.51%)		 54.67%
(75.35%)
複勝 55.52%
(80.09%)		 53.36%
(78.44%)		 57.52%
(82.08%)		 58.43%
(76.07%)		 64.33%
(92.36%)		 60.45%
(78.06%)		 52.91%
(79.77%)		 89.66%
(81.16%)
枠連 13.76%
(74.67%)		 12.13%
(75.46%)		 14.90%
(72.29%)		 18.99%
(99.24%)		 --
(--)		 13.98%
(60.81%)		 12.86%
(75.44%)		 30.49%
(76.91%)
馬連 9.96%
(65.54%)		 8.69%
(66.33%)		 11.10%
(64.22%)		 12.36%
(72.70%)		 20.38%
(87.83%)		 11.43%
(63.91%)		 8.50%
(64.19%)		 23.60%
(71.87%)
ワイド 24.18%
(73.08%)		 22.69%
(71.83%)		 25.91%
(75.45%)		 21.35%
(58.09%)		 40.13%
(78.85%)		 27.22%
(70.56%)		 21.64%
(73.52%)		 46.17%
(76.18%)
馬単 5.48%
(56.70%)		 4.93%
(58.16%)		 6.11%
(56.11%)		 4.49%
(43.93%)		 12.10%
(81.21%)		 6.77%
(66.80%)		 4.42%
(50.79%)		 23.60%
(70.33%)
三連複 6.59%
(71.77%)		 6.34%
(68.12%)		 7.00%
(78.69%)		 4.49%
(27.64%)		 13.38%
(90.45%)		 7.37%
(61.35%)		 5.70%
(74.01%)		 16.93%
(77.84%)
三連単 1.46%
(90.74%)		 1.02%
(49.79%)		 1.93%
(137.14%)		 1.12%
(30.67%)		 1.91%
(106.43%)		 1.65%
(58.74%)		 1.34%
(101.26%)		 16.93%
(74.78%)
総合 57.20%
(73.55%)		 55.56%
(68.63%)		 58.81%
(79.71%)		 58.43%
(57.05%)		 67.52%
(87.42%)		 60.90%
(66.51%)		 54.92%
(74.56%)		 89.96%
(74.93%)

この際に使ったパラメーターが分からなくなった(笑) イテレーション数3,000なのは分かるが、率は多分0.01だったのかな^^; モデルサイズが21MBなのでそんな気がするけど。

パラメータ / 指標 条件 1 条件 2 条件 3 条件 4 条件 5 条件 6 条件 7
NumberOfLeaves 79 95 95 111 127 255 351
NumberOfIterations 3,000 3,000 3,000 3,000 3,000 3,000 3,000
LearningRate 0.010 0.010 0.005 0.010 0.010 0.010 0.010
MaximumBin 127 127 127 127 127 127 127
MinimumExample 50 50 50 50 50 50 50
SubsampleFraction 0.800 0.800 0.800 0.800 0.800 0.800 0.800
FeatureFraction 0.75 0.75 0.75 0.75 0.75 0.75 0.75
ボーダー 300 300 300 300 300 300 300
乗数 0.95 0.95 0.95 0.95 0.95 0.95 0.95
NDCG@1 0.7667 0.7660 0.7635 0.7673 0.7669 0.7668 0.7665
NDCG@3 0.7725 0.7723 0.7701 0.7730 0.7731 0.7731 0.7736
NDCG@5 0.7889 0.7891 0.7867 0.7893 0.7895 0.7893 0.7895
NDCG@10 0.8498 0.8499 0.8482 0.8502 0.8504 0.8505 0.8507

なんか以前、葉を増やして出したモデルっぽいな。最近の走破タイムでは葉の数を絞ってたので忘れてるけど300以上も試してた記憶もあるからなぁ。

NumberOfLeaves: 335
NumberOfIterations: 3000
LearningRate: 0.01
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 50
SubsampleFraction: 0.8
FeatureFraction: 0.75
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

これ10回試したら最後に

[Loop 10] NDCG@1 = 0.7683
[Loop 10] NDCG@3 = 0.7740
[Loop 10] NDCG@5 = 0.7902
[Loop 10] NDCG@10 = 0.8509

が出た。

2,610R 1点 芝(1,278R) ダート(1,243R) 障害(89R) 8頭以下(157R) 9～12頭(665R) 13頭以上(1,788R) 多点
単勝 24.37%
(74.99%)		 23.87%
(74.20%)		 25.02%
(77.40%)		 22.47%
(52.58%)		 34.39%
(74.39%)		 27.82%
(71.64%)		 22.20%
(76.29%)		 54.90%
(75.18%)
複勝 54.90%
(78.89%)		 52.66%
(76.56%)		 57.12%
(81.78%)		 56.18%
(72.02%)		 64.33%
(85.92%)		 59.70%
(76.42%)		 52.29%
(79.19%)		 89.77%
(81.65%)
枠連 13.67%
(76.25%)		 12.13%
(81.24%)		 14.73%
(69.86%)		 18.99%
(103.16%)		 --
(--)		 13.38%
(58.06%)		 12.98%
(78.54%)		 30.28%
(78.02%)
馬連 10.04%
(64.96%)		 9.00%
(64.98%)		 10.94%
(64.22%)		 12.36%
(75.06%)		 21.02%
(80.89%)		 10.83%
(61.10%)		 8.78%
(64.99%)		 23.52%
(74.94%)
ワイド 24.44%
(73.08%)		 23.47%
(72.16%)		 25.66%
(75.07%)		 21.35%
(58.43%)		 40.76%
(77.45%)		 27.37%
(72.23%)		 21.92%
(73.01%)		 46.51%
(79.31%)
馬単 5.56%
(59.32%)		 4.69%
(49.66%)		 6.36%
(69.24%)		 6.74%
(59.44%)		 12.10%
(68.79%)		 6.92%
(62.26%)		 4.47%
(57.40%)		 23.52%
(72.10%)
三連複 7.05%
(77.72%)		 7.12%
(78.04%)		 7.16%
(80.98%)		 4.49%
(27.64%)		 15.29%
(116.94%)		 7.52%
(64.41%)		 6.15%
(79.23%)		 16.36%
(76.34%)
三連単 1.61%
(93.76%)		 1.25%
(57.75%)		 2.01%
(135.30%)		 1.12%
(30.67%)		 3.82%
(181.27%)		 1.95%
(49.79%)		 1.29%
(102.43%)		 16.36%
(71.54%)
総合 56.67%
(74.85%)		 54.85%
(69.10%)		 58.49%
(81.81%)		 57.30%
(59.26%)		 68.15%
(97.95%)		 60.30%
(64.49%)		 54.31%
(76.38%)		 90.04%
(73.89%)

やはり、この辺りなんだなぁ^^;

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2026/10/17

SQLite Version 3.54.1

1週間で新たなリリースがありました。前回の不具合報告から"Fixed"との事なので入れてみました。

フルセットアップは前回も問題なかったですが、前回よりちょっと早い? その後、最適化もしたんですが、何となくこれも早くなった気がする。で、問題だった検証も、ヤバい位早くなったと思う。これ、キャッシュとかの影響もきになるけど、単にフルセットアップ→最適化→検証と行っただけなので、キャッシュはされている気がしないのに^^; ちょっと信じ難いレベルの改善がされてる!

しかし、そもそも早いSQLiteなんだがまだまだ改善されるとか驚かされます。

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2026/10/10

メモるの忘れてた

サボってた訳ではなく単純にここに書くのを忘れてた^^; しかもどんなパラメーター試してたのかも忘れた(笑) 学習させたモデルは残ってるのでRMSEとかはファイル名で分かるけど...しかし、SQLiteをバージョンアップしたのが原因なのか、まあ、そもそも最初の検証時はデータがキャッシュされてないってもあって遅いんだが、それにしても異常に遅いorz 確か数分で終わるレベルの筈なんだが、そもそも1か月がまだ終わらないのは酷いな。

まさかとは思ったけど、SQLite Version 3.54.0にしたら激遅になったっぽい😱 今年の1月～9月の検証で１時間以上たっても3月にやっと入った程度だったのが、元に戻したら６分２８秒で検証完了(笑) で、ああっ、通常用走破タイム学習RMSE 1.5893は

2,427R 1点 芝(1,149R) ダート(1,189R) 障害(89R) 8頭以下(129R) 9～12頭(597R) 13頭以上(1,701R) 多点
単勝 20.19%
(69.93%)		 20.19%
(75.27%)		 20.86%
(66.78%)		 11.24%
(42.92%)		 28.68%
(77.29%)		 20.44%
(57.77%)		 19.46%
(73.63%)		 49.57%
(76.03%)
複勝 50.60%
(81.03%)		 47.00%
(78.19%)		 53.83%
(83.41%)		 53.93%
(85.84%)		 58.91%
(85.66%)		 51.93%
(77.44%)		 49.50%
(81.94%)		 85.41%
(80.63%)
枠連 12.13%
(78.91%)		 11.50%
(85.28%)		 12.73%
(73.76%)		 11.39%
(72.78%)		 --
(--)		 11.56%
(62.73%)		 11.64%
(80.09%)		 27.71%
(83.80%)
馬連 8.45%
(70.66%)		 7.83%
(79.63%)		 9.00%
(61.93%)		 8.99%
(71.46%)		 15.50%
(72.64%)		 8.21%
(58.31%)		 8.00%
(74.84%)		 20.27%
(77.86%)
ワイド 21.14%
(77.61%)		 18.62%
(71.32%)		 23.63%
(81.48%)		 20.22%
(107.08%)		 33.33%
(62.25%)		 23.45%
(82.11%)		 19.40%
(77.20%)		 41.20%
(79.85%)
馬単 4.08%
(63.07%)		 3.39%
(65.38%)		 5.05%
(65.57%)		 0.00%
(0.00%)		 7.75%
(76.98%)		 4.19%
(46.68%)		 3.76%
(67.77%)		 20.27%
(76.07%)
三連複 5.27%
(97.31%)		 4.26%
(107.21%)		 6.31%
(92.54%)		 4.49%
(33.26%)		 9.30%
(37.83%)		 6.03%
(80.74%)		 4.70%
(107.64%)		 14.09%
(71.25%)
三連単 0.70%
(61.07%)		 0.52%
(101.88%)		 0.93%
(26.21%)		 0.00%
(0.00%)		 1.55%
(56.28%)		 1.01%
(42.45%)		 0.53%
(67.97%)		 14.09%
(67.48%)
総合 52.25%
(74.90%)		 49.26%
(82.98%)		 55.00%
(68.93%)		 53.93%
(51.37%)		 63.57%
(66.99%)		 53.43%
(63.53%)		 50.97%
(78.89%)		 85.74%
(72.46%)

まだまだだけど少しは改善した。しかし、何してこうなったのか全く忘れてるので次の手が分からないorz

追記 2026.10.10 22:19

NumberOfLeaves: 95
NumberOfIterations: 1,500
LearningRate: 0.020
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 20
SubsampleFraction: 0.075
FeatureFraction: 0.980
L2Regularization: 0.500

これでRMSE 1.5878

2,427R 1点 芝(1,149R) ダート(1,189R) 障害(89R) 8頭以下(129R) 9～12頭(597R) 13頭以上(1,701R) 多点
単勝 20.19%
(72.18%)		 19.67%
(75.90%)		 21.11%
(69.55%)		 14.61%
(59.44%)		 25.58%
(64.11%)		 21.78%
(66.77%)		 19.22%
(74.70%)		 49.77%
(75.37%)
複勝 51.17%
(81.62%)		 48.13%
(81.06%)		 54.25%
(82.84%)		 49.44%
(72.47%)		 58.91%
(88.60%)		 55.95%
(83.05%)		 48.91%
(80.59%)		 86.65%
(81.91%)
枠連 12.27%
(77.44%)		 11.10%
(86.29%)		 13.62%
(72.12%)		 7.59%
(42.66%)		 --
(--)		 11.06%
(64.17%)		 11.99%
(77.68%)		 27.81%
(86.55%)
馬連 8.74%
(69.35%)		 7.92%
(76.23%)		 9.84%
(65.00%)		 4.49%
(38.65%)		 16.28%
(79.30%)		 7.87%
(58.74%)		 8.47%
(72.32%)		 20.56%
(79.45%)
ワイド 21.63%
(78.38%)		 18.80%
(73.37%)		 24.31%
(80.95%)		 22.47%
(108.88%)		 30.23%
(54.96%)		 23.95%
(83.79%)		 20.16%
(78.27%)		 41.94%
(82.40%)
馬単 4.37%
(64.96%)		 3.92%
(72.42%)		 5.13%
(62.61%)		 0.00%
(0.00%)		 7.75%
(57.60%)		 4.19%
(46.72%)		 4.17%
(71.92%)		 20.56%
(76.95%)
三連複 5.81%
(88.59%)		 5.13%
(103.20%)		 6.56%
(78.60%)		 4.49%
(33.26%)		 12.40%
(57.29%)		 6.87%
(88.49%)		 4.94%
(90.99%)		 14.50%
(88.29%)
三連単 0.74%
(45.01%)		 0.70%
(57.29%)		 0.84%
(36.52%)		 0.00%
(0.00%)		 2.33%
(53.72%)		 0.67%
(40.94%)		 0.65%
(45.78%)		 14.50%
(85.54%)
総合 52.41%
(72.13%)		 49.52%
(78.08%)		 55.34%
(68.50%)		 50.56%
(44.44%)		 60.47%
(65.08%)		 57.45%
(66.58%)		 50.03%
(74.03%)		 86.98%
(83.34%)

この辺りがそろそろ限界に近いのかも。いや、現行ML.NETではね。来月に期待してます。

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SQLite Version 3.54.0

ここしばらく動きが無かったけど、昨日(10/9)バージョンアップされたので早速入れてみた。本来はSourceGear.sqlite3を待つのですが、この更新タイミングが毎回微妙なので先行してSQLite本体のprecompiledダウンロードして適用。

で、早速フルセットアップさせてみた。

公式アナウンスでは4%改善との事。こちらでは、フルセットアップ時に毎回微妙にバックグラウンドで他の処理させてたり、そもそも時期が違えば期間も長くなるので正確な比較は出来ないけど、まあ、33分は速いのかな(笑)

追記 2026.10.10 11:41

セットアップって基本データの書込みばかりやってるだけなんだが、検証走らせると激遅でした。Version 3.53.4に戻しました。

追記 2026.10.14 20:06

SQLiteフォーラムに報告して既に修正済みとの事だけど、独自ビルドすれば修正されるんだろうけど、一旦様子見します。

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2026/10/07

ちょっとした言い訳(笑)

別に手を抜いてるとか、気力が失せた訳じゃないんです。やれるだけやるって気持ちは未だあるんです。しかし、ここまで来ると、11月10日にリリース予定の.NET11がどうしても気になる。ってのも、そもそもその段階でML.NET 6.0も正式リリース(現時点ではpreview)なり、もしかするとLightGBMも4.7が採用される。更に、元々.NET10で既に何度かリリースしてたのに、Visual Studioの.NET10の発行時のバグで.NET9にダウングレードを余儀なくされた。

モデルの学習は時間が掛かるのは周知の事実。一度はLightGBM 4.7でやってたのを現時点では4.6にもどしてる。この状態でモデルの学習を突き詰めても新たにML.NET 6.0の正式リリース及びLightGBM 4.7がって事になれば、全て最初からやり直しになる。

まあ、これら以外にも微妙に気になる.NET11からの新機能にも期待はしてます。なので、年内に大幅なアップグレードした物がリリース出来る様に今はちょっとだけ、その下準備(笑)

いやね、AIに何度も騙されたから、ローカルLLMとかって事でここ数日試しました。OllamaとかLM Studioとかね。Ollamaは8年前に組んだこのPCでは動きませんorz いや、CUDA関連かもなんだが、NVIDIAのドライバーの更新で何度か沼に嵌ってOSのクリーンインストールしてるので、試す勇気がない。更にLM Studio Bionicはグラボが古いのでもっさりした反応だし、そもそも学習されてるデータが若干古い。

サボるつもりは全く無いです。限られた時間でやれる事はやります😉

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2026/10/04

葉の数抑えた通常用走破タイム学習

葉の数を抑えると何度か回してってのは無意味っぽい。だいたい1回目が一番良い(笑)

パラメータ / 指標 条件 1 条件 2 条件 3 条件 4 条件 5 条件 6 条件 7
NumberOfLeaves 63 79 47 63 63 63 63
NumberOfIterations 1,500 1,500 1,500 1,500 1,500 1,500 1,500
LearningRate 0.020 0.020 0.020 0.020 0.020 0.020 0.020
MaximumBin 63 63 63 79 95 111 127
MinimumExample 50 50 50 50 50 50 50
SubsampleFraction 0.050 0.050 0.050 0.050 0.050 0.050 0.050
FeatureFraction 0.800 0.800 0.800 0.800 0.800 0.800 0.800
L2Regularization 0.500 0.500 0.500 0.500 0.500 0.500 0.500
RMSE 1.6048 1.6072 1.6217 1.6167 1.6073 1.6205 1.6061

追記 2026.10.4 21:22

NumberOfLeaves: 79
NumberOfIterations: 1,500
LearningRate: 0.020
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 30
SubsampleFraction: 0.025
FeatureFraction: 0.980
L2Regularization: 0.500

先月中旬頃の自分の記事確認してたら、こんな感じが良いモデル出てるって事で改めて5回程試すと確かにRMSE 1.5910～1.5931でした(笑) これでは4回目が一番だった。

2,427R 1点 芝(1,149R) ダート(1,189R) 障害(89R) 8頭以下(129R) 9～12頭(597R) 13頭以上(1,701R) 多点
単勝 19.90%
(69.19%)		 19.15%
(71.42%)		 20.94%
(66.73%)		 15.73%
(73.26%)		 27.13%
(69.38%)		 21.61%
(71.07%)		 18.75%
(68.51%)		 48.62%
(72.17%)
複勝 49.44%
(78.79%)		 45.43%
(75.31%)		 53.32%
(82.72%)		 49.44%
(71.24%)		 59.69%
(86.82%)		 52.60%
(80.44%)		 47.56%
(77.61%)		 85.04%
(78.90%)
枠連 11.31%
(78.43%)		 10.29%
(94.24%)		 12.38%
(66.64%)		 8.86%
(48.86%)		 --
(--)		 10.55%
(59.58%)		 10.93%
(80.57%)		 27.53%
(83.22%)
馬連 7.91%
(67.61%)		 7.22%
(76.01%)		 8.75%
(61.81%)		 5.62%
(36.85%)		 15.50%
(77.29%)		 7.71%
(61.73%)		 7.41%
(68.95%)		 19.61%
(70.77%)
ワイド 21.43%
(81.88%)		 18.10%
(75.92%)		 24.73%
(88.39%)		 20.22%
(71.80%)		 31.78%
(65.35%)		 23.62%
(81.16%)		 19.87%
(83.39%)		 39.93%
(75.69%)
馬単 4.12%
(60.97%)		 3.57%
(74.06%)		 4.79%
(51.49%)		 2.25%
(18.65%)		 8.53%
(78.68%)		 4.86%
(55.14%)		 3.53%
(61.67%)		 19.61%
(68.60%)
三連複 5.23%
(86.02%)		 3.92%
(96.59%)		 6.48%
(78.85%)		 5.62%
(45.39%)		 8.53%
(44.73%)		 6.03%
(75.01%)		 4.70%
(93.02%)		 13.84%
(76.44%)
三連単 0.95%
(84.14%)		 0.87%
(149.14%)		 1.01%
(26.02%)		 1.12%
(21.35%)		 3.10%
(113.80%)		 1.17%
(64.24%)		 0.71%
(88.87%)		 13.84%
(71.43%)
総合 50.97%
(75.85%)		 47.26%
(88.99%)		 54.67%
(65.32%)		 49.44%
(48.42%)		 62.02%
(76.58%)		 54.10%
(68.54%)		 49.03%
(77.82%)		 85.37%
(72.84%)

AIに言わせると、この辺りが純粋に走破タイムを予測させる限界だからと何度も言われ続け、いやね、既にその為の対処として相対タイムやランキングもためしてますって(笑) ただ、新馬戦用走破タイム学習はもう少し良い感じになってるから諦めきれてないんだよね。

SubsampleFractionを0.025→0.050にして5回試すとRMSE 1.5909～1.5932だった。

2,427R 1点 芝(1,149R) ダート(1,189R) 障害(89R) 8頭以下(129R) 9～12頭(597R) 13頭以上(1,701R) 多点
単勝 19.78%
(68.64%)		 19.76%
(73.91%)		 20.19%
(64.75%)		 14.61%
(52.58%)		 27.91%
(70.70%)		 22.11%
(66.65%)		 18.34%
(69.18%)		 48.95%
(76.39%)
複勝 49.65%
(79.57%)		 46.13%
(78.97%)		 52.73%
(79.98%)		 53.93%
(81.80%)		 63.57%
(93.80%)		 52.26%
(76.67%)		 47.68%
(79.51%)		 85.00%
(80.47%)
枠連 11.72%
(74.64%)		 10.90%
(83.85%)		 12.64%
(68.21%)		 8.86%
(51.27%)		 --
(--)		 10.55%
(55.73%)		 11.46%
(77.03%)		 27.31%
(81.11%)
馬連 8.16%
(76.46%)		 7.40%
(80.46%)		 9.00%
(75.14%)		 6.74%
(42.36%)		 17.05%
(77.52%)		 7.54%
(60.32%)		 7.70%
(82.04%)		 19.78%
(71.76%)
ワイド 21.63%
(89.17%)		 18.62%
(84.53%)		 24.56%
(93.92%)		 21.35%
(85.62%)		 34.88%
(68.53%)		 22.95%
(80.85%)		 20.16%
(93.65%)		 41.08%
(81.10%)
馬単 4.33%
(69.63%)		 4.18%
(79.96%)		 4.63%
(62.13%)		 2.25%
(36.63%)		 7.75%
(51.55%)		 5.36%
(72.13%)		 3.70%
(70.13%)		 19.78%
(70.25%)
三連複 5.27%
(75.96%)		 3.74%
(76.70%)		 6.73%
(72.09%)		 5.62%
(117.98%)		 8.53%
(34.65%)		 6.20%
(58.73%)		 4.70%
(85.14%)		 13.97%
(77.80%)
三連単 0.78%
(31.33%)		 0.52%
(31.98%)		 1.01%
(30.77%)		 1.12%
(30.67%)		 0.78%
(9.69%)		 1.51%
(74.77%)		 0.53%
(17.73%)		 13.97%
(77.01%)
総合 51.30%
(70.63%)		 48.30%
(73.62%)		 53.99%
(68.37%)		 53.93%
(62.52%)		 66.67%
(58.06%)		 53.43%
(68.23%)		 49.38%
(71.80%)		 85.37%
(76.56%)

RMSEの差はたったの0.0001なんだが、随分と違う感じだ。ひとつひとつでは判断難しいけど、総合的には的中率は向上してるのはその差かな?

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2026/10/03

迷走中

ズルズルと何度も繰り返して色々試すけど納得出来るモデルが出ない😞

パラメータ / 指標 条件 1 条件 2 条件 3 条件 4 条件 5 条件 6 条件 7
NumberOfLeaves 79 63 63 63 63 63 63
NumberOfIterations 5,000 3,000 3,000 3,000 3,000 3,000 3,000
LearningRate 0.010 0.010 0.010 0.010 0.010 0.010 0.010
MaximumBin 127 63 63 63 63 63 63
MinimumExample 20 20 20 30 40 50 60
SubsampleFraction 0.050 0.050 0.025 0.050 0.050 0.050 0.050
FeatureFraction 0.800 0.800 0.980 0.800 0.800 0.800 0.800
L2Regularization 0.500 0.500 0.500 0.500 0.500 0.500 0.500
R² （決定係数） 0.2263 0.2334 0.2323 0.2340 0.2340 0.2339 0.2334

まあ、葉の最小は30か40みたいなので30に絞って10回試す。でも、これってAIに毎回質問したりしてるけど、AIも所詮ネット上の情報なんかを元にしてるだけで、実際に全てのパターンを試してる訳じゃないので途方もないパターンの中から最適な設定を探すのは地道なパターン潰ししかない気はしてるんだよね。いや、まあ、それが面倒だからAutoMLが割る訳なんだが(笑)

もしかしたら、またAutoMLで24時間とか回してそのパラメーターを真似するのが最短なのかもしれないけど^^; 新馬戦用走破タイム学習だと葉の数が滅茶苦茶多かったんだよね。

30を10回やったが0.2330～0.2337しか出なかったorz なので40を10回スタートした。まあ、以前は40回とかやってた時だいたい20回程度で良いのが出た事もあったし、これ本当に運任せだから辛い😩なんだろなぁ、0.2334～0.2339しか出ない。でまあ、50を10回やったんだが、ここで、0.2332～0.2341でした😝

2,610R 1点 芝(1,278R) ダート(1,243R) 障害(89R) 8頭以下(157R) 9～12頭(665R) 13頭以上(1,788R) 多点
単勝 23.91%
(78.26%)		 23.94%
(82.92%)		 24.22%
(74.10%)		 19.10%
(69.44%)		 33.76%
(71.53%)		 25.11%
(68.42%)		 22.60%
(82.51%)		 53.45%
(76.84%)
複勝 54.87%
(80.56%)		 53.13%
(79.22%)		 56.40%
(81.63%)		 58.43%
(84.72%)		 63.06%
(83.50%)		 59.10%
(80.17%)		 52.57%
(80.44%)		 88.12%
(80.68%)
枠連 13.03%
(71.94%)		 11.95%
(68.62%)		 13.89%
(74.42%)		 15.19%
(80.51%)		 --
(--)		 13.98%
(70.39%)		 11.91%
(68.29%)		 28.66%
(72.86%)
馬連 9.66%
(66.23%)		 9.15%
(65.15%)		 10.06%
(66.99%)		 11.24%
(71.24%)		 20.38%
(97.20%)		 11.58%
(70.17%)		 8.00%
(62.05%)		 22.76%
(69.48%)
ワイド 23.68%
(76.26%)		 22.38%
(71.77%)		 25.26%
(82.08%)		 20.22%
(59.44%)		 37.58%
(74.78%)		 27.37%
(77.19%)		 21.09%
(76.04%)		 45.63%
(76.79%)
馬単 5.52%
(70.54%)		 5.16%
(73.52%)		 5.87%
(67.51%)		 5.62%
(70.00%)		 12.74%
(88.85%)		 6.77%
(71.50%)		 4.42%
(68.57%)		 22.76%
(69.10%)
三連複 6.32%
(74.61%)		 6.26%
(70.16%)		 6.44%
(77.38%)		 5.62%
(99.78%)		 13.38%
(95.22%)		 6.77%
(76.32%)		 5.54%
(72.16%)		 16.05%
(71.69%)
三連単 1.42%
(63.95%)		 1.17%
(42.94%)		 1.69%
(87.92%)		 1.12%
(30.67%)		 2.55%
(51.46%)		 2.26%
(87.28%)		 1.01%
(56.36%)		 16.05%
(65.71%)
総合 56.13%
(72.80%)		 54.38%
(69.30%)		 57.60%
(76.52%)		 60.67%
(70.58%)		 65.61%
(80.36%)		 59.85%
(75.18%)		 53.91%
(70.80%)		 88.35%
(69.53%)

でも、まだまだなモデルでしたorz

追記 2026.10.3 23:39

NumberOfLeaves: 31
NumberOfIterations: 3,000
LearningRate: 0.010
MaximumBinCountPerFeature: 63
MinimumExampleCountPerLeaf: 50
SubsampleFraction: 0.050
FeatureFraction: 0.800
L2Regularization: 0.500

葉の数を更に絞ってみたらR² 0.2371～0.2375だったので0.2375検証すると

2,610R 1点 芝(1,278R) ダート(1,243R) 障害(89R) 8頭以下(157R) 9～12頭(665R) 13頭以上(1,788R) 多点
単勝 24.02%
(76.18%)		 24.26%
(79.26%)		 24.14%
(74.30%)		 19.10%
(57.98%)		 32.48%
(71.08%)		 26.17%
(71.20%)		 22.48%
(78.47%)		 53.03%
(76.37%)
複勝 55.29%
(80.56%)		 54.54%
(81.55%)		 56.23%
(80.04%)		 52.81%
(73.60%)		 64.97%
(91.08%)		 60.45%
(81.31%)		 52.52%
(79.36%)		 88.24%
(81.03%)
枠連 13.46%
(75.75%)		 12.41%
(72.37%)		 14.39%
(78.95%)		 13.92%
(74.30%)		 --
(--)		 14.44%
(71.68%)		 12.30%
(72.81%)		 29.30%
(77.29%)
馬連 9.77%
(65.36%)		 9.47%
(67.28%)		 10.14%
(64.05%)		 8.99%
(56.18%)		 20.38%
(97.20%)		 11.73%
(70.99%)		 8.11%
(60.47%)		 22.91%
(74.36%)
ワイド 23.87%
(75.13%)		 22.38%
(71.10%)		 25.66%
(79.90%)		 20.22%
(66.52%)		 37.58%
(72.99%)		 27.97%
(79.94%)		 21.14%
(73.53%)		 45.36%
(78.11%)
馬単 5.17%
(59.82%)		 5.01%
(69.34%)		 5.31%
(49.77%)		 5.62%
(63.60%)		 10.19%
(58.03%)		 6.92%
(70.32%)		 4.08%
(56.08%)		 22.91%
(73.43%)
三連複 6.55%
(82.69%)		 6.42%
(74.92%)		 6.76%
(89.44%)		 5.62%
(99.78%)		 13.38%
(91.02%)		 7.22%
(87.53%)		 5.70%
(80.15%)		 16.40%
(73.56%)
三連単 1.38%
(68.10%)		 1.25%
(59.13%)		 1.53%
(79.99%)		 1.12%
(30.67%)		 2.55%
(51.46%)		 2.26%
(119.47%)		 0.95%
(50.45%)		 16.40%
(67.78%)
総合 56.70%
(72.91%)		 55.87%
(71.86%)		 57.68%
(74.53%)		 55.06%
(65.20%)		 66.88%
(76.12%)		 61.35%
(81.56%)		 54.08%
(68.91%)		 88.47%
(71.85%)

少しは良くなった。まだまだ改善の余地あるか?

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2026/10/02

相対タイム学習

ここ最近のパラメーターで相対タイム学習に挑んでますが、

NumberOfLeaves: 31
NumberOfIterations: 1,500
LearningRate: 0.050
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 20
SubsampleFraction: 0.500
FeatureFraction: 0.980
L2Regularization: 0.500

これで10回学習させて\(R^{2}\) 0.2288だと

2,610R 1点 芝(1,278R) ダート(1,243R) 障害(89R) 8頭以下(157R) 9～12頭(665R) 13頭以上(1,788R) 多点
単勝 23.60%
(76.21%)		 23.71%
(79.50%)		 23.89%
(74.22%)		 17.98%
(56.74%)		 31.85%
(67.39%)		 26.02%
(69.91%)		 21.98%
(79.32%)		 52.80%
(76.59%)
複勝 55.02%
(81.02%)		 53.91%
(80.20%)		 56.32%
(82.34%)		 52.81%
(74.16%)		 64.33%
(86.69%)		 60.60%
(82.24%)		 52.13%
(80.06%)		 88.35%
(80.94%)
枠連 12.48%
(68.05%)		 11.86%
(64.13%)		 13.12%
(72.68%)		 11.39%
(52.91%)		 --
(--)		 13.83%
(69.47%)		 11.24%
(63.53%)		 28.58%
(75.07%)
馬連 9.27%
(60.49%)		 9.15%
(60.52%)		 9.49%
(61.49%)		 7.87%
(46.29%)		 20.38%
(99.04%)		 11.28%
(69.32%)		 7.55%
(53.83%)		 22.22%
(73.03%)
ワイド 23.26%
(72.21%)		 22.22%
(66.60%)		 24.54%
(78.30%)		 20.22%
(67.53%)		 37.58%
(70.06%)		 27.97%
(80.86%)		 20.25%
(69.18%)		 45.06%
(76.93%)
馬単 4.83%
(53.25%)		 4.46%
(55.09%)		 5.23%
(52.27%)		 4.49%
(40.45%)		 10.83%
(88.22%)		 6.32%
(57.53%)		 3.75%
(48.59%)		 22.22%
(70.93%)
三連複 6.25%
(76.38%)		 5.95%
(82.28%)		 6.60%
(72.81%)		 5.62%
(41.69%)		 11.46%
(64.90%)		 6.62%
(62.47%)		 5.65%
(82.57%)		 15.36%
(69.27%)
三連単 1.46%
(93.93%)		 1.10%
(109.68%)		 1.77%
(78.67%)		 2.25%
(80.90%)		 2.55%
(102.04%)		 1.80%
(50.11%)		 1.23%
(109.52%)		 15.36%
(64.91%)
総合 56.21%
(72.75%)		 54.77%
(74.95%)		 57.76%
(71.59%)		 55.06%
(57.65%)		 66.24%
(82.62%)		 61.50%
(67.74%)		 53.36%
(73.32%)		 88.54%
(69.50%)

これを更に精度上げるにはってGeminiにぶつけると、

NumberOfLeaves: 63
NumberOfIterations: 3,000
LearningRate: 0.010
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 20
SubsampleFraction: 0.500
FeatureFraction: 0.800
L2Regularization: 0.500

これを試すと\(R^{2}\) 0.2339

2,610R 1点 芝(1,278R) ダート(1,243R) 障害(89R) 8頭以下(157R) 9～12頭(665R) 13頭以上(1,788R) 多点
単勝 23.56%
(77.59%)		 23.47%
(82.89%)		 24.05%
(73.68%)		 17.98%
(56.07%)		 31.85%
(68.22%)		 25.56%
(68.45%)		 22.09%
(81.81%)		 53.75%
(78.08%)
複勝 55.13%
(80.39%)		 53.99%
(80.43%)		 56.07%
(80.26%)		 58.43%
(81.80%)		 63.69%
(84.08%)		 60.00%
(80.48%)		 52.57%
(80.04%)		 88.28%
(80.70%)
枠連 13.03%
(71.22%)		 11.76%
(67.45%)		 14.23%
(75.50%)		 12.66%
(59.11%)		 --
(--)		 13.53%
(68.15%)		 12.08%
(68.18%)		 29.17%
(75.76%)
馬連 9.50%
(65.75%)		 9.08%
(64.98%)		 9.98%
(67.47%)		 8.99%
(52.81%)		 19.11%
(99.75%)		 11.13%
(66.66%)		 8.05%
(62.43%)		 23.45%
(72.81%)
ワイド 23.64%
(74.45%)		 22.07%
(65.40%)		 25.34%
(84.42%)		 22.47%
(65.28%)		 36.94%
(70.96%)		 27.07%
(75.61%)		 21.20%
(74.33%)		 45.79%
(76.73%)
馬単 5.25%
(69.81%)		 5.09%
(79.67%)		 5.47%
(61.31%)		 4.49%
(46.85%)		 10.83%
(92.61%)		 6.62%
(64.06%)		 4.25%
(69.94%)		 23.45%
(72.62%)
三連複 6.32%
(75.02%)		 6.03%
(66.40%)		 6.76%
(87.28%)		 4.49%
(27.64%)		 12.74%
(66.82%)		 6.62%
(72.09%)		 5.65%
(76.83%)		 15.94%
(73.48%)
三連単 1.42%
(56.84%)		 1.41%
(58.73%)		 1.45%
(56.76%)		 1.12%
(30.67%)		 2.55%
(105.73%)		 2.26%
(73.25%)		 1.01%
(46.44%)		 15.94%
(67.97%)
総合 56.36%
(71.39%)		 55.16%
(70.81%)		 57.28%
(73.32%)		 60.67%
(52.44%)		 66.24%
(84.02%)		 60.60%
(71.09%)		 53.91%
(70.00%)		 88.51%
(71.66%)

なんだけど、微妙な検証結果。

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2026/09/29

AutoMLのパラメーターを真似する

AutoMLのログにモデルをセーブする際にパラメーターを表示する機能追加を先日してあったのですが、それをパクってみようかと(笑)

NumberOfLeaves: 9000
NumberOfIterations: 1,500
LearningRate: 0.003
MaximumBinCountPerFeature: 399
MinimumExampleCountPerLeaf: 23
SubsampleFraction: 0.025
FeatureFraction: 0.980
L2Regularization: 0.500

モデルのRMSE 2.0384で

183R 1点 芝(129R) ダート(54R) 8頭以下(28R) 9～12頭(68R) 13頭以上(87R) 多点
単勝 24.59%
(100.66%)		 25.58%
(109.46%)		 22.22%
(79.63%)		 35.71%
(82.50%)		 30.88%
(133.09%)		 16.09%
(81.15%)		 48.63%
(68.94%)
複勝 51.91%
(97.92%)		 49.61%
(90.93%)		 57.41%
(114.63%)		 60.71%
(83.21%)		 55.88%
(101.47%)		 45.98%
(99.89%)		 81.42%
(84.37%)
枠連 9.52%
(107.55%)		 11.34%
(150.10%)		 6.00%
(25.00%)		 --
(--)		 16.18%
(168.68%)		 3.45%
(49.89%)		 24.49%
(105.28%)
馬連 7.65%
(98.25%)		 9.30%
(131.24%)		 3.70%
(19.44%)		 21.43%
(151.07%)		 8.82%
(139.71%)		 2.30%
(48.85%)		 19.13%
(106.27%)
ワイド 18.58%
(99.62%)		 19.38%
(76.20%)		 16.67%
(155.56%)		 39.29%
(113.93%)		 20.59%
(85.29%)		 10.34%
(106.21%)		 38.25%
(100.00%)
馬単 4.92%
(122.40%)		 5.43%
(161.63%)		 3.70%
(28.70%)		 14.29%
(181.43%)		 5.88%
(241.18%)		 1.15%
(10.57%)		 19.13%
(105.73%)
三連複 4.92%
(108.03%)		 4.65%
(70.23%)		 5.56%
(198.33%)		 7.14%
(177.86%)		 5.88%
(60.00%)		 3.45%
(123.10%)		 10.93%
(76.99%)
三連単 2.19%
(193.77%)		 2.33%
(212.79%)		 1.85%
(148.33%)		 7.14%
(534.29%)		 1.47%
(183.68%)		 1.15%
(92.07%)		 10.93%
(57.91%)
総合 53.55%
(116.24%)		 51.94%
(124.53%)		 57.41%
(96.87%)		 60.71%
(189.18%)		 60.29%
(139.14%)		 45.98%
(76.47%)		 81.42%
(75.61%)

で、まあ、基本的には似てる事は似てるけど、通常用走破タイム学習にも試すとそのままではOOMでしたorz 少し抑えて

NumberOfLeaves: 2048
NumberOfIterations: 1,500
LearningRate: 0.003
MaximumBinCountPerFeature: 399
MinimumExampleCountPerLeaf: 100
SubsampleFraction: 0.100
FeatureFraction: 0.980
L2Regularization: 0.500

とするとRMSE 1.6526で

2,427R 1点 芝(1,149R) ダート(1,189R) 障害(89R) 8頭以下(129R) 9～12頭(597R) 13頭以上(1,701R) 多点
単勝 20.07%
(70.88%)		 18.80%
(74.97%)		 20.86%
(65.71%)		 25.84%
(87.08%)		 32.56%
(78.06%)		 20.10%
(57.14%)		 19.11%
(75.16%)		 47.88%
(76.86%)
複勝 49.61%
(81.13%)		 45.52%
(77.94%)		 53.24%
(84.03%)		 53.93%
(83.71%)		 61.24%
(86.98%)		 53.94%
(81.19%)		 47.21%
(80.67%)		 85.13%
(81.38%)
枠連 11.31%
(74.13%)		 7.57%
(59.33%)		 14.50%
(87.25%)		 12.66%
(72.03%)		 --
(--)		 12.90%
(80.69%)		 10.11%
(67.61%)		 25.72%
(79.48%)
馬連 7.91%
(67.01%)		 5.05%
(52.28%)		 10.43%
(79.26%)		 11.24%
(93.60%)		 14.73%
(63.72%)		 10.05%
(88.61%)		 6.64%
(59.68%)		 18.54%
(67.49%)
ワイド 20.27%
(77.05%)		 15.84%
(67.57%)		 24.31%
(86.25%)		 23.60%
(76.40%)		 34.88%
(75.74%)		 24.79%
(89.90%)		 17.58%
(72.63%)		 39.31%
(78.52%)
馬単 4.08%
(59.40%)		 2.26%
(47.68%)		 5.63%
(68.10%)		 6.74%
(94.61%)		 10.08%
(102.02%)		 5.03%
(78.26%)		 3.29%
(49.55%)		 18.54%
(66.81%)
三連複 5.03%
(68.12%)		 3.57%
(59.21%)		 6.31%
(74.71%)		 6.74%
(95.06%)		 10.85%
(69.77%)		 6.53%
(85.93%)		 4.06%
(61.74%)		 12.86%
(65.70%)
三連単 1.15%
(58.92%)		 0.44%
(45.44%)		 1.85%
(74.76%)		 1.12%
(21.35%)		 3.10%
(128.99%)		 1.68%
(70.84%)		 0.82%
(49.42%)		 12.86%
(62.07%)
総合 50.76%
(69.53%)		 46.39%
(60.57%)		 54.75%
(77.45%)		 53.93%
(78.06%)		 62.79%
(86.47%)		 55.28%
(79.07%)		 48.27%
(64.56%)		 85.33%
(67.33%)

これではまだまだな感じですね。で、ちょっと調整して

NumberOfLeaves: 512
NumberOfIterations: 1,500
LearningRate: 0.010
MaximumBinCountPerFeature: 399
MinimumExampleCountPerLeaf: 300
SubsampleFraction: 0.300
FeatureFraction: 0.980
L2Regularization: 0.500

だとRMSE 1.5971で

2,427R 1点 芝(1,149R) ダート(1,189R) 障害(89R) 8頭以下(129R) 9～12頭(597R) 13頭以上(1,701R) 多点
単勝 19.53%
(71.52%)		 19.15%
(73.23%)		 19.93%
(69.75%)		 19.10%
(73.15%)		 26.36%
(76.36%)		 21.27%
(69.38%)		 18.40%
(71.90%)		 46.89%
(76.12%)
複勝 49.07%
(82.81%)		 45.26%
(78.99%)		 52.99%
(86.88%)		 46.07%
(77.64%)		 56.59%
(93.41%)		 54.10%
(84.57%)		 46.74%
(81.38%)		 83.68%
(80.92%)
枠連 10.77%
(75.80%)		 9.49%
(85.54%)		 12.11%
(70.21%)		 7.59%
(33.54%)		 --
(--)		 11.06%
(70.90%)		 10.05%
(73.19%)		 23.72%
(70.70%)
馬連 6.88%
(72.04%)		 6.18%
(75.66%)		 7.65%
(70.60%)		 5.62%
(44.72%)		 14.73%
(99.38%)		 7.20%
(68.81%)		 6.17%
(71.11%)		 17.88%
(65.14%)
ワイド 19.12%
(80.82%)		 16.45%
(76.34%)		 21.78%
(87.22%)		 17.98%
(53.26%)		 34.11%
(80.78%)		 20.44%
(74.79%)		 17.52%
(82.95%)		 38.20%
(78.41%)
馬単 3.54%
(65.43%)		 3.31%
(76.28%)		 3.87%
(58.46%)		 2.25%
(18.65%)		 7.75%
(73.72%)		 4.02%
(53.58%)		 3.06%
(68.97%)		 17.88%
(63.50%)
三連複 4.70%
(78.94%)		 4.09%
(81.84%)		 5.21%
(77.01%)		 5.62%
(67.30%)		 15.50%
(123.26%)		 5.53%
(72.36%)		 3.59%
(77.88%)		 11.70%
(65.49%)
三連単 0.95%
(68.81%)		 1.13%
(103.38%)		 0.76%
(38.96%)		 1.12%
(21.35%)		 5.43%
(288.99%)		 1.17%
(65.08%)		 0.53%
(53.42%)		 11.70%
(60.32%)
総合 50.60%
(74.51%)		 46.91%
(81.33%)		 54.42%
(69.88%)		 47.19%
(48.92%)		 60.47%
(119.41%)		 55.11%
(69.94%)		 48.27%
(72.60%)		 83.85%
(65.35%)

なんだが、今日はもう遅いので寝ます。また継続してやらなきゃです。

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2026/09/27

第60回スプリンターズステークス

朝からってか、もう随分と前から色々やってますが、全然時間が足りない。相変わらず、独自に予想してみた。

もうさ、全く予想に集中出来ない状態なので、参加してみただけです。

今週中にはモデルまとめてリリース出来ればと思ってます。

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葉の数抑えたランキング学習

ランキング学習では葉の数を多くしてたのですが、ここの所やってる葉の数を抑えたらどうなるかで

NumberOfLeaves: 79
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 30
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7670
[Loop 1] NDCG@3 = 0.7721
[Loop 1] NDCG@5 = 0.7887
[Loop 1] NDCG@10 = 0.8497

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 24.63%
(73.93%)		 24.26%
(76.13%)		 25.04%
(72.77%)		 24.14%
(58.39%)		 32.89%
(70.34%)		 26.99%
(72.75%)		 23.06%
(74.67%)		 54.57%
(75.24%)
複勝 55.66%
(79.18%)		 53.79%
(78.00%)		 57.20%
(80.40%)		 60.92%
(79.20%)		 67.11%
(93.69%)		 61.81%
(80.95%)		 52.41%
(77.30%)		 89.81%
(82.08%)
枠連 13.98%
(78.51%)		 12.56%
(83.73%)		 15.11%
(73.37%)		 16.67%
(83.72%)		 --
(--)		 13.04%
(64.69%)		 13.52%
(79.03%)		 30.04%
(75.87%)
馬連 10.15%
(79.92%)		 9.58%
(100.30%)		 10.72%
(60.93%)		 10.34%
(53.22%)		 21.48%
(94.43%)		 10.58%
(65.67%)		 9.03%
(83.97%)		 23.26%
(72.22%)
ワイド 24.08%
(74.39%)		 23.06%
(77.95%)		 25.29%
(71.70%)		 21.84%
(60.80%)		 40.27%
(82.95%)		 26.84%
(71.30%)		 21.69%
(74.80%)		 46.84%
(74.79%)
馬単 5.70%
(91.56%)		 5.27%
(118.51%)		 6.06%
(65.49%)		 6.90%
(69.54%)		 11.41%
(94.16%)		 6.75%
(70.32%)		 4.83%
(99.20%)		 23.26%
(70.09%)
三連複 6.32%
(65.36%)		 6.30%
(65.83%)		 6.46%
(67.51%)		 4.60%
(28.28%)		 16.11%
(125.57%)		 7.21%
(63.28%)		 5.17%
(61.03%)		 15.96%
(74.27%)
三連単 1.48%
(79.04%)		 1.36%
(96.92%)		 1.55%
(61.09%)		 2.30%
(73.68%)		 4.03%
(226.31%)		 2.15%
(66.70%)		 1.02%
(71.15%)		 15.96%
(70.66%)
総合 57.38%
(77.73%)		 55.55%
(87.23%)		 58.92%
(69.13%)		 62.07%
(63.09%)		 69.80%
(112.49%)		 62.27%
(69.46%)		 54.51%
(77.64%)		 90.05%
(72.50%)

まあ、試しの1回でなので悪くないかな。

NumberOfLeaves: 63
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 30
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7655
[Loop 1] NDCG@3 = 0.7712
[Loop 1] NDCG@5 = 0.7883
[Loop 1] NDCG@10 = 0.8494

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 23.93%
(72.47%)		 23.70%
(74.45%)		 24.14%
(71.29%)		 24.14%
(60.46%)		 32.21%
(68.79%)		 25.92%
(68.27%)		 22.49%
(74.33%)		 54.41%
(74.75%)
複勝 55.54%
(79.34%)		 53.87%
(78.22%)		 56.71%
(80.25%)		 63.22%
(82.76%)		 64.43%
(90.87%)		 61.50%
(80.54%)		 52.58%
(77.93%)		 89.38%
(81.20%)
枠連 13.77%
(77.66%)		 12.46%
(80.48%)		 14.94%
(75.97%)		 14.10%
(64.23%)		 --
(--)		 13.19%
(62.33%)		 13.17%
(78.79%)		 29.48%
(75.34%)
馬連 9.88%
(65.17%)		 9.34%
(70.04%)		 10.47%
(61.50%)		 9.20%
(46.67%)		 22.82%
(100.81%)		 10.89%
(64.57%)		 8.40%
(62.38%)		 23.15%
(71.05%)
ワイド 24.32%
(71.61%)		 23.38%
(69.49%)		 25.45%
(73.78%)		 21.84%
(71.61%)		 41.61%
(79.06%)		 27.61%
(74.05%)		 21.64%
(70.07%)		 46.92%
(75.69%)
馬単 5.23%
(58.90%)		 4.87%
(62.96%)		 5.56%
(54.35%)		 5.75%
(64.14%)		 10.74%
(69.87%)		 6.75%
(71.37%)		 4.20%
(53.35%)		 23.15%
(68.93%)
三連複 6.52%
(69.76%)		 6.30%
(63.72%)		 6.87%
(78.91%)		 4.60%
(28.28%)		 14.09%
(74.70%)		 7.52%
(63.90%)		 5.51%
(71.52%)		 16.12%
(74.53%)
三連単 1.37%
(97.92%)		 0.88%
(64.64%)		 1.80%
(133.76%)		 2.30%
(73.68%)		 2.01%
(30.94%)		 1.99%
(62.06%)		 1.08%
(116.86%)		 16.12%
(72.10%)
総合 57.42%
(74.06%)		 55.95%
(70.31%)		 58.43%
(78.74%)		 64.37%
(61.44%)		 67.11%
(73.58%)		 62.27%
(68.38%)		 54.80%
(75.65%)		 89.62%
(72.96%)

葉の数削り過ぎかな^^; では、葉の数は戻して葉の最小少し増やして

NumberOfLeaves: 79
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 50
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7663
[Loop 1] NDCG@3 = 0.7719
[Loop 1] NDCG@5 = 0.7887
[Loop 1] NDCG@10 = 0.8496

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 24.24%
(72.94%)		 23.86%
(75.23%)		 24.63%
(71.57%)		 24.14%
(59.31%)		 34.23%
(76.78%)		 25.92%
(68.48%)		 22.77%
(74.27%)		 54.72%
(75.58%)
複勝 55.19%
(78.78%)		 53.15%
(77.09%)		 56.79%
(80.25%)		 62.07%
(82.53%)		 64.43%
(88.66%)		 60.89%
(79.60%)		 52.30%
(77.64%)		 89.66%
(81.92%)
枠連 13.98%
(79.62%)		 12.65%
(83.57%)		 15.02%
(75.54%)		 16.67%
(86.67%)		 --
(--)		 13.34%
(62.18%)		 13.40%
(81.43%)		 30.52%
(76.57%)
馬連 10.27%
(80.00%)		 9.74%
(97.04%)		 10.72%
(63.46%)		 11.49%
(66.90%)		 23.49%
(100.94%)		 10.89%
(63.88%)		 8.92%
(84.20%)		 23.54%
(71.50%)
ワイド 24.20%
(72.74%)		 23.30%
(73.81%)		 25.37%
(72.81%)		 20.69%
(56.32%)		 40.94%
(78.66%)		 27.61%
(71.26%)		 21.52%
(72.78%)		 46.99%
(76.75%)
馬単 5.85%
(88.86%)		 5.35%
(117.88%)		 6.22%
(59.71%)		 8.05%
(80.34%)		 13.42%
(108.86%)		 6.90%
(71.72%)		 4.83%
(93.51%)		 23.54%
(69.61%)
三連複 6.83%
(80.21%)		 6.30%
(68.83%)		 7.45%
(90.32%)		 5.75%
(102.07%)		 14.77%
(103.22%)		 8.13%
(85.74%)		 5.68%
(76.22%)		 16.28%
(75.35%)
三連単 1.60%
(118.35%)		 1.20%
(82.79%)		 1.96%
(158.00%)		 2.30%
(73.68%)		 4.03%
(181.95%)		 1.99%
(115.28%)		 1.25%
(114.11%)		 16.28%
(72.53%)
総合 57.14%
(83.99%)		 55.31%
(84.55%)		 58.59%
(84.01%)		 63.22%
(75.84%)		 67.11%
(105.58%)		 61.66%
(77.27%)		 54.63%
(84.27%)		 89.89%
(73.58%)

指数的には落ちた感じだけど、三連系が良くなった。葉の最小を減らすと

NumberOfLeaves: 79
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 20
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7643
[Loop 1] NDCG@3 = 0.7715
[Loop 1] NDCG@5 = 0.7885
[Loop 1] NDCG@10 = 0.8492

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 24.12%
(73.38%)		 23.54%
(73.97%)		 24.80%
(74.19%)		 22.99%
(53.56%)		 31.54%
(68.26%)		 26.23%
(67.41%)		 22.71%
(76.03%)		 54.92%
(76.85%)
複勝 55.85%
(79.85%)		 53.47%
(77.55%)		 57.69%
(81.90%)		 64.37%
(84.25%)		 65.77%
(91.21%)		 61.96%
(80.84%)		 52.75%
(78.52%)		 89.73%
(81.45%)
枠連 13.55%
(75.87%)		 11.62%
(74.15%)		 15.02%
(76.64%)		 17.95%
(88.08%)		 --
(--)		 12.42%
(59.95%)		 13.17%
(77.33%)		 29.70%
(75.77%)
馬連 10.03%
(80.70%)		 8.94%
(93.68%)		 11.05%
(68.65%)		 11.49%
(63.10%)		 22.15%
(102.15%)		 10.28%
(63.60%)		 8.92%
(85.22%)		 23.34%
(72.45%)
ワイド 24.28%
(74.84%)		 22.75%
(74.03%)		 26.02%
(75.86%)		 21.84%
(72.07%)		 37.58%
(70.54%)		 27.76%
(75.38%)		 21.86%
(75.00%)		 46.45%
(75.40%)
馬単 5.39%
(83.65%)		 4.55%
(106.00%)		 6.14%
(62.36%)		 6.90%
(60.80%)		 11.41%
(107.65%)		 5.98%
(58.54%)		 4.66%
(90.92%)		 23.34%
(70.37%)
三連複 6.60%
(74.05%)		 6.46%
(74.87%)		 6.87%
(76.46%)		 4.60%
(28.28%)		 15.44%
(122.62%)		 7.82%
(75.90%)		 5.39%
(69.25%)		 16.47%
(77.54%)
三連単 1.48%
(74.33%)		 1.04%
(52.14%)		 1.96%
(100.14%)		 1.15%
(31.38%)		 4.03%
(226.31%)		 1.84%
(110.41%)		 1.14%
(48.11%)		 16.47%
(73.15%)
総合 57.53%
(77.10%)		 55.39%
(78.38%)		 59.17%
(77.03%)		 65.52%
(59.83%)		 68.46%
(112.67%)		 62.42%
(74.01%)		 54.80%
(75.05%)		 89.93%
(74.14%)

ダメだ(笑) 葉の最小は30～50の間辺りが良いのかな? もしくはそれ以上かも?

NumberOfLeaves: 79
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 40
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7653
[Loop 1] NDCG@3 = 0.7717
[Loop 1] NDCG@5 = 0.7882
[Loop 1] NDCG@10 = 0.8495

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 24.75%
(75.39%)		 24.34%
(76.37%)		 25.20%
(75.30%)		 24.14%
(62.41%)		 32.89%
(70.20%)		 26.38%
(71.35%)		 23.45%
(77.32%)		 54.49%
(74.58%)
複勝 56.01%
(79.90%)		 54.51%
(79.60%)		 57.04%
(79.96%)		 63.22%
(83.33%)		 64.43%
(88.93%)		 61.04%
(80.75%)		 53.44%
(78.82%)		 89.73%
(80.79%)
枠連 13.55%
(75.94%)		 11.90%
(78.15%)		 14.76%
(73.10%)		 17.95%
(88.08%)		 --
(--)		 12.27%
(54.48%)		 13.23%
(79.44%)		 29.61%
(73.36%)
馬連 9.84%
(76.41%)		 8.86%
(92.39%)		 10.72%
(60.96%)		 11.49%
(63.10%)		 20.81%
(94.30%)		 9.97%
(56.46%)		 8.86%
(82.28%)		 23.11%
(69.01%)
ワイド 23.97%
(74.15%)		 22.67%
(75.43%)		 25.53%
(74.19%)		 20.69%
(55.06%)		 38.93%
(76.51%)		 26.07%
(68.62%)		 21.92%
(75.99%)		 46.29%
(73.34%)
馬単 5.62%
(84.50%)		 4.79%
(105.78%)		 6.38%
(63.35%)		 6.90%
(75.29%)		 12.75%
(102.89%)		 6.29%
(63.71%)		 4.77%
(90.65%)		 23.11%
(67.00%)
三連複 6.44%
(70.12%)		 6.07%
(60.97%)		 6.87%
(82.05%)		 5.75%
(34.48%)		 13.42%
(71.74%)		 7.52%
(63.34%)		 5.45%
(72.50%)		 16.35%
(72.69%)
三連単 1.41%
(103.43%)		 1.04%
(72.38%)		 1.72%
(137.39%)		 2.30%
(73.68%)		 2.68%
(86.85%)		 1.53%
(50.09%)		 1.25%
(124.58%)		 16.35%
(69.08%)
総合 57.53%
(80.03%)		 56.19%
(80.17%)		 58.51%
(80.83%)		 63.22%
(66.65%)		 67.11%
(84.49%)		 61.35%
(63.60%)		 55.31%
(85.20%)		 89.85%
(70.67%)

中途半端はダメか? まあ、実際1発勝負だと的確には分からないけど^^; 秋のGIには間に合えばと頑張ったつもりが、既に今日はスプリンターズorz

NumberOfLeaves: 79
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 80
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7650
[Loop 1] NDCG@3 = 0.7717
[Loop 1] NDCG@5 = 0.7882
[Loop 1] NDCG@10 = 0.8494

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 24.16%
(72.86%)		 24.42%
(76.91%)		 24.06%
(70.28%)		 21.84%
(50.92%)		 34.23%
(74.30%)		 25.92%
(70.08%)		 22.66%
(73.78%)		 54.57%
(74.85%)
複勝 55.78%
(79.55%)		 54.11%
(79.02%)		 57.12%
(80.10%)		 60.92%
(79.43%)		 65.77%
(94.03%)		 61.35%
(80.66%)		 52.87%
(77.91%)		 89.11%
(81.29%)
枠連 13.90%
(77.75%)		 12.28%
(77.35%)		 15.11%
(77.69%)		 17.95%
(84.10%)		 --
(--)		 12.73%
(58.79%)		 13.52%
(80.22%)		 30.13%
(76.80%)
馬連 9.88%
(64.86%)		 9.10%
(70.14%)		 10.56%
(59.74%)		 11.49%
(60.69%)		 22.15%
(100.87%)		 9.97%
(57.59%)		 8.80%
(64.50%)		 23.61%
(69.94%)
ワイド 24.16%
(70.86%)		 22.67%
(69.15%)		 25.86%
(72.64%)		 21.84%
(70.69%)		 40.27%
(78.79%)		 26.84%
(70.81%)		 21.81%
(70.21%)		 46.80%
(75.25%)
馬単 5.35%
(61.32%)		 4.95%
(64.60%)		 5.81%
(59.11%)		 4.60%
(44.94%)		 12.75%
(91.81%)		 5.98%
(61.96%)		 4.49%
(58.50%)		 23.61%
(67.96%)
三連複 6.87%
(75.59%)		 6.38%
(64.95%)		 7.53%
(89.86%)		 4.60%
(28.28%)		 15.44%
(95.91%)		 7.98%
(67.75%)		 5.74%
(76.77%)		 16.24%
(66.85%)
三連単 1.44%
(109.13%)		 0.96%
(69.47%)		 1.96%
(155.33%)		 1.15%
(31.38%)		 3.36%
(101.54%)		 1.69%
(51.66%)		 1.19%
(131.05%)		 16.24%
(62.08%)
総合 57.46%
(76.47%)		 56.03%
(71.34%)		 58.59%
(83.12%)		 62.07%
(55.94%)		 68.46%
(91.04%)		 61.81%
(64.91%)		 54.91%
(79.12%)		 89.31%
(67.24%)

結局は葉の最小は50が一番良いのかな? 一応60も試して置くか。

NumberOfLeaves: 79
NumberOfIterations: 1500
LearningRate: 0.02
MaximumBinCountPerFeature: 127
MinimumExampleCountPerLeaf: 60
FeatureFraction: 0.75
SubsampleFraction: 0.8
CustomGains: 0～300 = 0点, 301～1000は0.95乗に0～1000点

[Loop 1] NDCG@1 = 0.7653
[Loop 1] NDCG@3 = 0.7719
[Loop 1] NDCG@5 = 0.7884
[Loop 1] NDCG@10 = 0.8495

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%
(73.68%)		 23.78%
(75.19%)		 24.71%
(73.46%)		 22.99%
(54.94%)		 32.21%
(68.39%)		 25.77%
(67.12%)		 22.94%
(76.55%)		 54.96%
(75.45%)
複勝 55.58%
(79.21%)		 54.03%
(78.53%)		 56.71%
(79.78%)		 62.07%
(80.92%)		 67.79%
(97.99%)		 61.35%
(79.08%)		 52.41%
(77.67%)		 89.38%
(81.36%)
枠連 13.85%
(77.27%)		 12.00%
(74.07%)		 15.45%
(80.23%)		 15.38%
(76.92%)		 --
(--)		 12.58%
(56.58%)		 13.52%
(80.41%)		 30.13%
(75.59%)
馬連 10.11%
(69.30%)		 8.94%
(67.17%)		 11.29%
(72.38%)		 10.34%
(56.55%)		 20.13%
(91.95%)		 10.12%
(58.30%)		 9.26%
(71.45%)		 23.46%
(73.07%)
ワイド 24.12%
(72.28%)		 22.59%
(67.57%)		 25.70%
(76.69%)		 24.14%
(78.16%)		 38.93%
(72.82%)		 25.31%
(65.89%)		 22.43%
(74.60%)		 46.68%
(76.77%)
馬単 5.35%
(63.47%)		 4.47%
(58.80%)		 6.30%
(69.61%)		 4.60%
(44.60%)		 9.40%
(64.90%)		 5.52%
(55.55%)		 4.94%
(66.29%)		 23.46%
(71.13%)
三連複 6.87%
(80.10%)		 6.62%
(77.24%)		 7.20%
(84.27%)		 5.75%
(62.64%)		 14.77%
(119.33%)		 7.36%
(68.39%)		 6.02%
(81.11%)		 16.16%
(75.00%)
三連単 1.41%
(104.04%)		 0.96%
(77.64%)		 1.88%
(136.28%)		 1.15%
(31.38%)		 2.68%
(170.20%)		 1.69%
(53.77%)		 1.19%
(117.05%)		 16.16%
(71.37%)
総合 57.34%
(77.42%)		 55.87%
(71.99%)		 58.43%
(84.11%)		 63.22%
(60.55%)		 70.47%
(97.94%)		 61.96%
(63.08%)		 54.51%
(80.64%)		 89.62%
(73.17%)

何とも微妙な結果だ。まあ、50で更に攻めるかな。

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