{"id":2481,"date":"2022-03-06T19:57:56","date_gmt":"2022-03-06T10:57:56","guid":{"rendered":"https:\/\/aiacademy.jp\/media\/?p=2481"},"modified":"2025-06-12T15:49:04","modified_gmt":"2025-06-12T06:49:04","slug":"optuna-%e5%85%a5%e9%96%80-%e3%83%8f%e3%82%a4%e3%83%91%e3%83%bc%e3%83%91%e3%83%a9%e3%83%a1%e3%83%bc%e3%82%bf%e3%82%92%e8%87%aa%e5%8b%95%e6%9c%80%e9%81%a9%e5%8c%96%e3%81%97%e3%81%a6%e3%81%bf%e3%82%88","status":"publish","type":"post","link":"https:\/\/aiacademy.jp\/media\/?p=2481","title":{"rendered":"Optuna \u5165\u9580 \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u81ea\u52d5\u6700\u9069\u5316\u3057\u3066\u307f\u3088\u3046\uff01"},"content":{"rendered":"\n<div class=\"toc\">    <div id=\"toc_container\" class=\"sgb-toc--no-bullets js-smooth-scroll\" data-dialog-title=\"Table of Contents\">\n      <p class=\"toc_title\">\u76ee\u6b21 <\/p>\n      <ul class=\"toc_list\">  <li class=\"first\">    <a href=\"#i-0\">Optuna \u3068\u306f<\/a>  <\/li>  <li>    <a href=\"#i-1\">Optuna\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/a>  <\/li>  <li>    <a href=\"#i-2\">K\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u5b9f\u65bd\u3059\u308b<\/a>  <\/li>  <li>    <a href=\"#i-3\">Optuna\u3067\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6700\u9069\u5316\u3059\u308b<\/a>  <\/li>  <li class=\"last\">    <a href=\"#i-4\">\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30bf\u30fc\u63a2\u7d22\u3067\u7279\u5b9a\u3057\u305f\u5024\u3092\u8a2d\u5b9a\u3059\u308b<\/a>  <\/li><\/ul>\n      \n    <\/div><\/div><div class=\"toc\"><p><\/p>\n<\/div><h2 class=\"wp-block-heading\" id=\"i-0\">Optuna \u3068\u306f<\/h2>\n\n\n\n<p><a href=\"https:\/\/optuna.org\/\" rel=\"nofollow noopener\" target=\"_blank\">Optuna<\/a>\uff08\u30aa\u30d7\u30c1\u30e5\u30ca\uff09\u3068\u306f\u3001&nbsp;Preferred Networks\u793e\u304c\u958b\u767a\u3057\u3066\u3044\u308b\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u6700\u9069\u5316\u3092\u81ea\u52d5\u5316\u3059\u308b\u305f\u3081\u306e\u30d5\u30ec\u30fc\u30e0\u30ef\u30fc\u30af\u3067\u3059\u3002<a href=\"https:\/\/optuna.readthedocs.io\/en\/stable\/tutorial\/index.html\" rel=\"nofollow noopener\" target=\"_blank\">\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u30da\u30fc\u30b8\u306f\u3053\u3061\u3089<\/a>\u304b\u3089\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<p><span class=\"keiko_green\">\u3053\u306e\u30b5\u30a4\u30c8\u306f\u3001Python\u3084\u751f\u6210AI\u306a\u3069\u3092\u5b66\u3079\u308b\u30aa\u30f3\u30e9\u30a4\u30f3\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u30b9\u30af\u30fc\u30eb AI Academy Bootcamp\u304c\u904b\u55b6\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-1\">Optuna\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb<\/h2>\n\n\n\n<p><\/p>\n\n\n\n<p>pip\u30b3\u30de\u30f3\u30c9\u3067\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>pip install optuna<\/code><\/pre>\n\n\n\n<p>Jupyter Notebook\u3082\u3057\u304f\u306fGoogle Colab\u304b\u3089\u5b9f\u884c\u3059\u308b\u5834\u5408\u306f\u3001\u5148\u982d\u306b!\u3092\u4ed8\u3051\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>!pip install optuna<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-2\">K\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u5b9f\u65bd\u3059\u308b<\/h2>\n\n\n\n<p>\u5148\u305a\u306fOptuna\u3092\u7528\u3044\u308b\u524d\u306bK\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u5b9f\u65bd\u3057\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n\n\n\n<p>\u4eca\u56de\u306f\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u3092\u7528\u3044\u3066\u3001scikit-learn\u306b\u4ed8\u5c5e\u306e\u4e73\u304c\u3093\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u3066\u5206\u985e\u5668\u3092\u4f5c\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score \nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport optuna\nfrom sklearn.model_selection import cross_validate\n\nfrom sklearn import datasets\ncancer = datasets.load_breast_cancer()\n\nX = cancer.data\ny = 1- cancer.target\n\nX_train, X_test, y_train, y_test = train_test_split(\n    X, y, test_size=0.2, shuffle=True, random_state=123)\n\n# \u89e3\u8aac\n# solver\u306f\u3001\u6700\u9069\u5316\u554f\u984c\u3067\u4f7f\u7528\u3059\u308b\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u3059\u3002\n# \u4eca\u56de\u306f\u3001'lbfgs'\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\u4ed6\u306b\u306f 'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'\u304c\u3042\u308a\u307e\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f'lbfgs'\u3067\u3059\u306e\u3067\u3001\u660e\u793a\u7684\u306b'lbfgs'\u3092\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\n# \u6700\u5927\u53cd\u5fa9\u56de\u6570\u306f max_iter \u3067\u8a2d\u5b9a\u304c\u53ef\u80fd\u3067\u3059\u3002\u30c7\u30d5\u30a9\u30eb\u30c8\u306f100\u56de\u3067\u3059\u304c\u3001\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u307e\u307e\u3067\u3059\u3068\u8b66\u544a\u304c\u51fa\u3066\u3057\u307e\u3046\u305f\u3081\u3001max_iter\u309210000\u306b\u6307\u5b9a\u3057\u3066\u3044\u307e\u3059\u3002\nmodel = LogisticRegression(solver='lbfgs',max_iter=10000)\n\n# \u8a13\u7df4\nmodel.fit(X_train, y_train)\n\n# \u4e88\u6e2c\npred = model.predict(X_test)\n\n# \u6b63\u89e3\u7387\u3092\u51fa\u529b\naccuracy = 100.0 * accuracy_score(y_test, pred)\nprint(\"\u6b63\u89e3\u7387: {}\".format(accuracy))<\/code><\/pre>\n\n\n\n<p><strong>\u51fa\u529b\u7d50\u679c<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>\u6b63\u89e3\u7387: 98.24561403508771<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from sklearn.model_selection import cross_val_score\n\n# 5\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u306b\u3088\u308b\u6c4e\u5316\u6027\u80fd\u306e\u8a55\u4fa1\nscores = cross_val_score(model, X_train, y_train, cv=5)\n\n# \u8a55\u4fa1\u7d50\u679c\u306e\u51fa\u529b\nprint(\"5\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u306b\u3088\u308b\u6c4e\u5316\u6027\u80fd\u306e\u8a55\u4fa1: {}\".format(100 * scores.mean()))<\/code><\/pre>\n\n\n\n<p><meta charset=\"utf-8\"><strong>\u51fa\u529b\u7d50\u679c<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>5\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u306b\u3088\u308b\u6c4e\u5316\u6027\u80fd\u306e\u8a55\u4fa1: 94.94505494505493<\/code><\/pre>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-3\"><meta charset=\"utf-8\">Optuna\u3067\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6700\u9069\u5316\u3059\u308b<\/h2>\n\n\n\n<p><meta charset=\"utf-8\">Optuna\u3067\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6700\u9069\u5316\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<p><meta charset=\"utf-8\">Optuna\u306e\u57fa\u672c\u7684\u306a\u4f7f\u3044\u65b9\u3068\u3057\u3066\u3001<\/p>\n\n\n\n<p><code>optuna.create_study()<\/code>\u3092\u5b9f\u884c\u3057\u3001<code>optuna.study<\/code>\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u308a\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u5f15\u6570\u306bdirection\u306b\u306f\u3001\u2019maximize\u2019\u3084\u2019minimize\u2019\u3092\u6e21\u305b\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u305d\u306e\u5f8c\u3001<meta charset=\"utf-8\"><code>study.optimize()<\/code>\u306b\u3001\u95a2\u6570\u3092\u6e21\u3057\u3066\u6700\u9069\u5316\u3057\u307e\u3059\u3002<\/p>\n\n\n\n<p>\u4eca\u56de\u306fObjective\u30af\u30e9\u30b9\u3092\u4f5c\u6210\u3057\u3001<meta charset=\"utf-8\">objective\u3067\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u5316\u3057\u3066\u3044\u308b\u305f\u3081\u3001<meta charset=\"utf-8\">objective\u3092\u5f15\u6570\u306b\u6e21\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score \nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import cross_validate\n\nimport numpy as np\nimport optuna\n\nclass Objective:\n    def __init__(self, X, y):\n        # \u5909\u6570X,y\u306e\u521d\u671f\u5316\n        self.X = X\n        self.y = y\n\n    def __call__(self, trial):\n        # \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u8a2d\u5b9a\n        params = {\n            # \u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u6307\u5b9a\n            'solver' : trial.suggest_categorical('solver', ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']),\n            # \u6b63\u5247\u5316\u306e\u5f37\u3055\u3092\u6307\u5b9a\uff080.0001\u304b\u308910\u307e\u3067\uff09\n            'C': trial.suggest_loguniform('C', 0.0001, 10),\n            # \u6700\u5927\u53cd\u5fa9\u56de\u6570\uff08\uff0a\u30bd\u30eb\u30d0\u30fc\u304c\u53ce\u675f\u3059\u308b\u307e\u3067\uff09\n            'max_iter': trial.suggest_int('max_iter', 100, 100000)\n            }\n\n        model = LogisticRegression(**params)\n\n        # \u8a55\u4fa1\u6307\u6a19\u3068\u3057\u3066\u6b63\u89e3\u7387\u306e\u6700\u5927\u5316\u3092\u76ee\u6307\u3059\n        scores = cross_validate(model,\n                                X=self.X, \n                                y=self.y,\n                                scoring='accuracy', # \u6b63\u89e3\u7387\u3092\u6307\u5b9a\uff08https:\/\/scikit-learn.org\/stable\/modules\/model_evaluation.html#scoring-parameter\uff09\n                                n_jobs=-1) # \u4e26\u884c\u3057\u3066\u5b9f\u884c\u3059\u308b\u30b8\u30e7\u30d6\u306e\u6570\uff08-1\u306f\u5168\u3066\u306e\u30d7\u30ed\u30bb\u30c3\u30b5\u3092\u4f7f\u7528\uff09\n        return scores['test_score'].mean()\n\n# \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a2\u7d22\nobjective = Objective(X_train, y_train)\nstudy = optuna.create_study(direction='maximize') # \u6700\u5927\u5316\nstudy.optimize(objective, timeout=60)\n\n# \u30d9\u30b9\u30c8\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u51fa\u529b\nprint('params:', study.best_params)<\/code><\/pre>\n\n\n\n<p>\u5b9f\u884c\u5f8c\u3001\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u63a2\u7d22\u304c\u7d42\u308f\u308a\u3001<meta charset=\"utf-8\">\u30d9\u30b9\u30c8\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u51fa\u529b\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<p><meta charset=\"utf-8\">\u30d9\u30b9\u30c8\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u51fa\u529b\u3057\u3066\u3044\u308b\u3053\u3068\u306f\u3001<meta charset=\"utf-8\"><code>best_params<\/code> \u3092\u51fa\u529b\u3059\u308b\u3053\u3068\u3067\u3001\u30d9\u30b9\u30c8\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>study.best_params<\/code><\/pre>\n\n\n\n<p><meta charset=\"utf-8\"><strong>\u51fa\u529b\u7d50\u679c<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>{'C': 8.688219513706178, 'max_iter': 34661, 'solver': 'liblinear'}<\/code><\/pre>\n\n\n\n<p>\u4e0a\u8a18\u306f\u8f9e\u66f8\u578b\u306e\u305f\u3081\u3001key\u3092\u6307\u5b9a\u3057\u3066\u51fa\u529b\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>print(study.best_params['solver'])\nprint(study.best_params['max_iter'])\nprint(study.best_params['C'])<\/code><\/pre>\n\n\n\n<p><meta charset=\"utf-8\"><strong>\u51fa\u529b\u7d50\u679c<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>liblinear\n34661\n8.688219513706178<\/code><\/pre>\n\n\n\n<p>\u3061\u306a\u307f\u306b\u3001\u4e00\u756a\u826f\u304b\u3063\u305f\u30b9\u30b3\u30a2\u306f\u4e0b\u8a18\u3067\u7b97\u51fa\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>print(study.best_value)<\/code><\/pre>\n\n\n\n<p><meta charset=\"utf-8\"><strong>\u51fa\u529b\u7d50\u679c<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>0.953846153846154<\/code><\/pre>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"i-4\">\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30bf\u30fc\u63a2\u7d22\u3067\u7279\u5b9a\u3057\u305f\u5024\u3092\u8a2d\u5b9a\u3059\u308b<\/h2>\n\n\n\n<p><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>from sklearn.metrics import confusion_matrix, accuracy_score\n<meta charset=\"utf-8\">from sklearn.metrics import precision_score, recall_score\n\nmodel = LogisticRegression(\n    # \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u63a2\u7d22\u3067\u7279\u5b9a\u3057\u305f\u5024\u3092\u8a2d\u5b9a\n    solver = study.best_params['solver'],\n    C = study.best_params['C'],\n    max_iter = study.best_params['max_iter']\n)\n\nmodel.fit(X_train, y_train)\npred = model.predict(X_test)\n# \u6b63\u89e3\u7387\u306e\u51fa\u529b\nprint(\"Accuracy: {:.5f}\".format(100 * accuracy_score(y_test, pred)))\n\n# \u6df7\u540c\u884c\u5217\u306e\u51fa\u529b\nprint(confusion_matrix(y_test, pred))\n\n# \u9069\u5408\u7387\nprint(\"Precision: {:.5f}\".format(100 * precision_score(y_test, pred)))\n# \u518d\u73fe\u7387\nprint(\"Recall: {:.5f}\".format(100 * recall_score(y_test, pred)))<\/code><\/pre>\n\n\n\n<p><meta charset=\"utf-8\"><strong>\u51fa\u529b\u7d50\u679c<\/strong><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>Accuracy: 98.24561\n[[73  0]\n [ 2 39]]\nPrecision: 100.00000\nRecall: 95.12195<\/code><\/pre>\n\n\n\n<p><strong><span class=\"keiko_green\">LINE\u516c\u5f0f\u3067\u306f\u3001AI\u3084\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u306b\u95a2\u3059\u308b\u6700\u65b0\u60c5\u5831\u3092\u914d\u4fe1\u3057\u3066\u3044\u307e\u3059\u3002<\/span><\/strong><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><a href=\"https:\/\/lin.ee\/3E4GzWk\" target=\"_blank\" rel=\" noreferrer noopener nofollow\"><img loading=\"lazy\" decoding=\"async\" width=\"1012\" height=\"613\" src=\"https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2025\/01\/lineb.png\" alt=\"\" class=\"wp-image-7550\" style=\"width:180px;height:auto\" srcset=\"https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2025\/01\/lineb.png 1012w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2025\/01\/lineb-300x182.png 300w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2025\/01\/lineb-768x465.png 768w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2025\/01\/lineb-940x569.png 940w\" sizes=\"auto, (max-width: 1012px) 100vw, 1012px\"><\/a><\/figure>\n\n\n\n<p><a href=\"https:\/\/aiacademy.jp\/bootcamp\" target=\"_blank\" rel=\"noopener\" title=\"\">AI Academy Bootcamp<\/a> \u3067\u306f<strong><span class=\"keiko_blue\">AI\u30fb\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u3001\u6a5f\u68b0\u5b66\u7fd2\u306e\u5b9f\u8df5\u529b\u3092\u9ad8\u3081\u308b\u51684\u30b3\u30fc\u30b940\u6642\u9593\u4ee5\u4e0a\u306e\u52d5\u753b\u304c\u898b\u653e\u984c\uff01<\/span><\/strong>AI\u306e\u5b66\u7fd2\u306b\u5fc5\u9808\u306ePython\u306e\u5b66\u7fd2\u304b\u3089\u59cb\u307e\u308a\u3001\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u30fb\u6a5f\u68b0\u5b66\u7fd2\u306a\u3069\u3001\u76ee\u7684\u306b\u5fdc\u3058\u305f\u5e45\u5e83\u3044\u5206\u91ce\u3092\u30ab\u30d0\u30fc\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n<figure class=\"wp-block-image\"><a href=\"https:\/\/aiacademy.jp\/bootcamp\/\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"341\" src=\"https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-1024x341.png\" alt=\"\" class=\"wp-image-691\" srcset=\"https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-1024x341.png 1024w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-300x100.png 300w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-768x256.png 768w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi-940x313.png 940w, https:\/\/aiacademy.jp\/media\/wp-content\/uploads\/2021\/12\/bootcamp_ad_72ppi.png 1200w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\"><\/a><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>\u76ee\u6b21 Optuna \u3068\u306f Optuna\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb K\u5206\u5272\u4ea4\u5dee\u691c\u8a3c\u3092\u5b9f\u65bd\u3059\u308b Optuna\u3067\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6700\u9069\u5316\u3059\u308b \u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30bf\u30fc\u63a2\u7d22\u3067\u7279\u5b9a\u3057\u305f\u5024\u3092\u8a2d\u5b9a\u3059\u308b Optuna \u3068\u306f Optuna\uff08\u30aa\u30d7\u30c1\u30e5\u30ca\uff09\u3068 &#8230; <\/p>\n","protected":false},"author":1,"featured_media":2485,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[130],"tags":[],"class_list":{"0":"post-2481","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-130"},"_links":{"self":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts\/2481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2481"}],"version-history":[{"count":28,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts\/2481\/revisions"}],"predecessor-version":[{"id":7580,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/posts\/2481\/revisions\/7580"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=\/wp\/v2\/media\/2485"}],"wp:attachment":[{"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2481"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2481"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiacademy.jp\/media\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}