我已经创建了自己的函数来记录所有超参数调整的尝试，以下信息是从 10 折交叉验证中收集的。
但我正在努力找出哪种型号最好。
+----+-------------------+-------------------+-------------------+-------------------+
| | Validation | Val StDev | Train Err | Holdout |
+----+-------------------+-------------------+-------------------+-------------------+
| 0 | 0.899393281242345 | 0.02848162454242 | 1 | 0.903572035647507 |
+----+-------------------+-------------------+-------------------+-------------------+
| 1 | 0.902138248122673 | 0.029520127182082 | 1 | 0.924233269690891 |
+----+-------------------+-------------------+-------------------+-------------------+
| 2 | 0.899394809813502 | 0.025173568322695 | 0.918663669124935 | 0.909288194444444 |
+----+-------------------+-------------------+-------------------+-------------------+
| 3 | 0.897228682965021 | 0.030714865334356 | 1 | 0.908866279069768 |
+----+-------------------+-------------------+-------------------+-------------------+
| 4 | 0.909270070641525 | 0.027056183719667 | 1 | 0.924575965355467 |
+----+-------------------+-------------------+-------------------+-------------------+
| 5 | 0.891001537843704 | 0.032846295144796 | 1 | 0.903091978426922 |
+----+-------------------+-------------------+-------------------+-------------------+
| 6 | 0.895784577401649 | 0.032132196798841 | 1 | 0.884848484848485 |
+----+-------------------+-------------------+-------------------+-------------------+
| 7 | 0.88188105768332 | 0.033952319596936 | 0.910316431235621 | 0.903091978426922 |
+----+-------------------+-------------------+-------------------+-------------------+
| 8 | 0.920584479640099 | 0.027520133628529 | 0.936918085663145 | 0.924575965355467 |
+----+-------------------+-------------------+-------------------+-------------------+
| 9 | 0.887347364032516 | 0.030797370441503 | 0.900531622363285 | 0.903091978426922 |
+----+-------------------+-------------------+-------------------+-------------------+
| 10 | 0.876942399956479 | 0.043318142049256 | 1 | 0.868971836419753 |
+----+-------------------+-------------------+-------------------+-------------------+
| 11 | 0.900452899973248 | 0.033120692366442 | 0.924324942576064 | 0.886904761904762 |
+----+-------------------+-------------------+-------------------+-------------------+
| 12 | 0.889635597754135 | 0.023388005619559 | 1 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 13 | 0.899270803213788 | 0.026083641971929 | 1 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 14 | 0.886812435037192 | 0.033456079535065 | 0.904149376999805 | 0.898247322297955 |
+----+-------------------+-------------------+-------------------+-------------------+
| 15 | 0.884982783710439 | 0.029572747271092 | 0.901110070564378 | 0.903091978426922 |
+----+-------------------+-------------------+-------------------+-------------------+
| 16 | 0.896948178627522 | 0.026483462928863 | 0.919101870462519 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 17 | 0.891278476391404 | 0.030334266939915 | 1 | 0.888614341085271 |
+----+-------------------+-------------------+-------------------+-------------------+
| 18 | 0.909092365260866 | 0.031848098756772 | 1 | 0.898247322297955 |
+----+-------------------+-------------------+-------------------+-------------------+
| 19 | 0.889032812279866 | 0.028580171007027 | 1 | 0.903572035647507 |
+----+-------------------+-------------------+-------------------+-------------------+
| 20 | 0.890821503056501 | 0.03250894068403 | 0.935473681065598 | 0.889619742654119 |
+----+-------------------+-------------------+-------------------+-------------------+
| 21 | 0.908662067002155 | 0.02515678884091 | 1 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 22 | 0.894626358857844 | 0.038437447908009 | 0.921035110317957 | 0.907956547269524 |
+----+-------------------+-------------------+-------------------+-------------------+
| 23 | 0.904503845292009 | 0.03112232540355 | 0.922738180662704 | 0.903091978426922 |
+----+-------------------+-------------------+-------------------+-------------------+
| 24 | 0.893363641701947 | 0.032000273114453 | 1 | 0.897729496966138 |
+----+-------------------+-------------------+-------------------+-------------------+
| 25 | 0.891379560352061 | 0.032525437349441 | 0.916932513590361 | 0.892891134050809 |
+----+-------------------+-------------------+-------------------+-------------------+
| 26 | 0.905999138236311 | 0.027801373368529 | 1 | 0.913722347684612 |
+----+-------------------+-------------------+-------------------+-------------------+
| 27 | 0.892155761699392 | 0.028942257106962 | 1 | 0.898740310077519 |
+----+-------------------+-------------------+-------------------+-------------------+
| 28 | 0.90547992240768 | 0.034616407726398 | 1 | 0.888072054527751 |
+----+-------------------+-------------------+-------------------+-------------------+
| 29 | 0.854504389713449 | 0.045352425614533 | 1 | 0.816845180136319 |
+----+-------------------+-------------------+-------------------+-------------------+
| 30 | 0.854329853134155 | 0.047548722046064 | 0.912076317693916 | 0.828930724012203 |
+----+-------------------+-------------------+-------------------+-------------------+
| 31 | 0.919465770658208 | 0.029180215562696 | 0.931409372636061 | 0.938637698179683 |
+----+-------------------+-------------------+-------------------+-------------------+
| 32 | 0.90057318637927 | 0.026049221971696 | 1 | 0.919367283950617 |
+----+-------------------+-------------------+-------------------+-------------------+
| 33 | 0.895446367880531 | 0.033041859254943 | 1 | 0.913722347684612 |
+----+-------------------+-------------------+-------------------+-------------------+
| 34 | 0.884125762352326 | 0.03697539365293 | 0.897337858027344 | 0.897729496966138 |
+----+-------------------+-------------------+-------------------+-------------------+
| 35 | 0.883233039971663 | 0.034111835608482 | 0.893720586886425 | 0.913722347684612 |
+----+-------------------+-------------------+-------------------+-------------------+
| 36 | 0.898831686569679 | 0.042604871968562 | 1 | 0.863619885443604 |
+----+-------------------+-------------------+-------------------+-------------------+
| 37 | 0.909826578207203 | 0.029949272123959 | 1 | 0.898247322297955 |
+----+-------------------+-------------------+-------------------+-------------------+
| 38 | 0.881913627468284 | 0.036432833984006 | 0.893183972214204 | 0.918992248062016 |
+----+-------------------+-------------------+-------------------+-------------------+
| 39 | 0.893848891847337 | 0.02592119349599 | 1 | 0.90842259006816 |
+----+-------------------+-------------------+-------------------+-------------------+
| 40 | 0.855511803338288 | 0.045600097937187 | 1 | 0.816845180136319 |
+----+-------------------+-------------------+-------------------+-------------------+
| 41 | 0.856261861628324 | 0.046589739419035 | 1 | 0.811284379041902 |
+----+-------------------+-------------------+-------------------+-------------------+
| 42 | 0.892329922600147 | 0.027143842917071 | 1 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 43 | 0.883558035554916 | 0.031100578488573 | 0.903223129534581 | 0.898740310077519 |
+----+-------------------+-------------------+-------------------+-------------------+
| 44 | 0.853943064191891 | 0.045073764302981 | 1 | 0.816845180136319 |
+----+-------------------+-------------------+-------------------+-------------------+
| 45 | 0.888853496341911 | 0.027961101762991 | 1 | 0.913722347684612 |
+----+-------------------+-------------------+-------------------+-------------------+
| 46 | 0.897113661181162 | 0.036022289370872 | 0.921272431864044 | 0.897729496966138 |
+----+-------------------+-------------------+-------------------+-------------------+
| 47 | 0.891468488082473 | 0.028579769797201 | 1 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 48 | 0.898831686569679 | 0.042604871968562 | 1 | 0.863619885443604 |
+----+-------------------+-------------------+-------------------+-------------------+
| 49 | 0.902910285816319 | 0.025031947763032 | 1 | 0.908866279069768 |
+----+-------------------+-------------------+-------------------+-------------------+
| 50 | 0.876865550417646 | 0.04283766065777 | 0.941524627838129 | 0.868971836419753 |
+----+-------------------+-------------------+-------------------+-------------------+
| 51 | 0.88091084510941 | 0.030430567351611 | 0.902847063239986 | 0.893421723610403 |
+----+-------------------+-------------------+-------------------+-------------------+
| 52 | 0.881680572698977 | 0.031246268640075 | 0.902192589536189 | 0.892891134050809 |
+----+-------------------+-------------------+-------------------+-------------------+
| 53 | 0.855040900830318 | 0.046858153140763 | 1 | 0.816845180136319 |
+----+-------------------+-------------------+-------------------+-------------------+
| 54 | 0.894438862703006 | 0.040891854948011 | 0.920792225979698 | 0.907956547269524 |
+----+-------------------+-------------------+-------------------+-------------------+
| 55 | 0.891904027574454 | 0.031645714271463 | 1 | 0.898740310077519 |
+----+-------------------+-------------------+-------------------+-------------------+
| 56 | 0.886050671635633 | 0.032472146105505 | 0.899909848784576 | 0.908866279069768 |
+----+-------------------+-------------------+-------------------+-------------------+
| 57 | 0.881980625144301 | 0.03563700286777 | 0.892637674766258 | 0.918992248062016 |
+----+-------------------+-------------------+-------------------+-------------------+
| 58 | 0.891270767582537 | 0.03142082216981 | 1 | 0.898740310077519 |
+----+-------------------+-------------------+-------------------+-------------------+
| 59 | 0.910664399342078 | 0.025582535272637 | 0.927959603815499 | 0.893421723610403 |
+----+-------------------+-------------------+-------------------+-------------------+
| 60 | 0.888100544552359 | 0.026544114270193 | 1 | 0.918597857838364 |
+----+-------------------+-------------------+-------------------+-------------------+
| 61 | 0.896690074843623 | 0.034624649065343 | 1 | 0.913292822803036 |
+----+-------------------+-------------------+-------------------+-------------------+
| 62 | 0.887338053809583 | 0.030621509271507 | 1 | 0.918992248062016 |
+----+-------------------+-------------------+-------------------+-------------------+
| 63 | 0.897753456871316 | 0.025062963044505 | 0.916156729004089 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 64 | 0.896456912702229 | 0.030808038532288 | 0.907756656209691 | 0.909288194444444 |
+----+-------------------+-------------------+-------------------+-------------------+
| 65 | 0.883460118896986 | 0.037339407800874 | 0.89706586511388 | 0.897729496966138 |
+----+-------------------+-------------------+-------------------+-------------------+
| 66 | 0.878395246090765 | 0.034089155345539 | 0.896519056428997 | 0.913722347684612 |
+----+-------------------+-------------------+-------------------+-------------------+
| 67 | 0.910664399342078 | 0.025582535272637 | 0.927959603815499 | 0.893421723610403 |
+----+-------------------+-------------------+-------------------+-------------------+
| 68 | 0.895947305636744 | 0.027392857919265 | 1 | 0.91413103898301 |
+----+-------------------+-------------------+-------------------+-------------------+
| 69 | 0.891138031159789 | 0.032237486772592 | 1 | 0.903572035647507 |
+----+-------------------+-------------------+-------------------+-------------------+
| 70 | 0.885255027194677 | 0.031984990817382 | 0.90200648514247 | 0.903572035647507 |
+----+-------------------+-------------------+-------------------+-------------------+
| 71 | 0.908735508831696 | 0.027210512830753 | 1 | 0.913722347684612 |
+----+-------------------+-------------------+-------------------+-------------------+
| 72 | 0.88713294586216 | 0.029978466529712 | 1 | 0.903572035647507 |
+----+-------------------+-------------------+-------------------+-------------------+
| 73 | 0.901055027327092 | 0.033359546159533 | 0.923523486623107 | 0.887502446662752 |
+----+-------------------+-------------------+-------------------+-------------------+
Traing err - 估计器拟合并在训练数据上预测的平均准确度得分
验证- esitmator 的平均准确度得分适合训练并在测试数据集上预测。
Val StDev - 基于在训练和测试数据集上预测的 esitmator 的准确度得分的标准差。
坚持（测试）错误- 坚持准确度得分（完全看不见的数据）
有了这种格式的信息 - 如何确定最佳的估算器选择？关于模型选择的信息太多了，很难全部吸收。
我的目标是在训练误差和验证误差之间产生最小的差异吗？即将学习曲线的线尽可能靠近。
训练误差 100% 的准确率是一件坏事吗？（它基本上记住了数据）我应该忽略所有这些模型吗？
我应该使用 1 sd 规则从最大平均验证分数中选择 1 SD 的估计器......然后选择保持和验证之间方差最小的模型？
有没有更好的方法来决定哪种型号最好？你会选择什么型号？