{"id":10300,"date":"2026-10-30T12:52:00","date_gmt":"2026-10-30T03:52:00","guid":{"rendered":"https:\/\/since2020.jp\/media\/?p=10300"},"modified":"2026-10-30T12:52:00","modified_gmt":"2026-10-30T03:52:00","slug":"parametertuning","status":"publish","type":"post","link":"https:\/\/since2020.jp\/media\/parametertuning\/","title":{"rendered":"LightGBM\u30fbXGBoost\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5fb9\u5e95\u89e3\u8aac \u2015 \u6570\u5024\u3092\u5909\u3048\u308b\u3068\u3069\u3046\u306a\u308b\uff1f"},"content":{"rendered":"\n

\u306f\u3058\u3081\u306b<\/h2>\n\n\n\n

LightGBM \u3084 XGBoost \u3067\u30e2\u30c7\u30eb\u3092\u4f5c\u308b\u3068\u304d\u3001learning_rate \u3084 num_leaves \u306a\u3069\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u300c\u3088\u304f\u4f7f\u308f\u308c\u308b\u5024\u300d\u306e\u307e\u307e\u3001\u610f\u5473\u3092\u5341\u5206\u306b\u7406\u89e3\u3057\u306a\u3044\u307e\u307e\u8a2d\u5b9a\u3057\u3066\u3044\u308b\u30b1\u30fc\u30b9\u306f\u5c11\u306a\u304f\u3042\u308a\u307e\u305b\u3093\u3002Optuna \u306a\u3069\u3067\u81ea\u52d5\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u3057\u3066\u3044\u3066\u3082\u3001\u5404\u30d1\u30e9\u30e1\u30fc\u30bf\u304c\u4f55\u3092\u3057\u3066\u3044\u308b\u306e\u304b\u304c\u5206\u304b\u3089\u306a\u3044\u3068\u3001\u7d50\u679c\u3092\u8aac\u660e\u3057\u305f\u308a\u3001\u63a2\u7d22\u7bc4\u56f2\u3092\u6c7a\u3081\u305f\u308a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3002<\/p>\n\n\n\n

\u3053\u306e\u8a18\u4e8b\u3067\u306f\u3001LightGBM \u3068 XGBoost \u306e\u4e3b\u8981\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u306b\u3064\u3044\u3066\u3001\u5f79\u5272\u3068\u6570\u5024\u3092\u5909\u3048\u308b\u3068\u3069\u3046\u306a\u308b\u304b\u30921\u3064\u305a\u3064\u6574\u7406\u3057\u3066\u89e3\u8aac\u3057\u307e\u3059\u3002\u3042\u308f\u305b\u3066\u30012\u3064\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u540d\u3084\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306e\u9055\u3044\u3082\u307e\u3068\u3081\u307e\u3059\u3002<\/p>\n\n\n\n

\u306a\u304a\u30013\u3064\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u30c7\u30d5\u30a9\u30eb\u30c8\u8a2d\u5b9a\u3067\u6bd4\u8f03\u3057\u305f\u7d50\u679c\u306f LightGBM\u30fbXGBoost\u30fbCatBoost\u3092\u30aa\u30fc\u30d7\u30f3\u30c7\u30fc\u30bf\u3067\u6bd4\u8f03\u3057\u3066\u307f\u305f<\/a> \u3067\u7d39\u4ecb\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u306e\u57fa\u672c\uff1a\u904e\u5b66\u7fd2\u3068\u5b66\u7fd2\u4e0d\u8db3<\/h2>\n\n\n\n

\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3068\u306f\u3001\u30e2\u30c7\u30eb\u306e\u8907\u96d1\u3055\u3092\u8abf\u6574\u3057\u3066\u3001\u6b21\u306e2\u3064\u306e\u72b6\u614b\u306e\u30d0\u30e9\u30f3\u30b9\u3092\u53d6\u308b\u4f5c\u696d\u3067\u3059\u3002<\/p>\n\n\n\n

\u72b6\u614b<\/th>\u610f\u5473<\/th>\u898b\u5206\u3051\u65b9<\/th><\/tr><\/thead>
\u904e\u5b66\u7fd2<\/td>\u5b66\u7fd2\u30c7\u30fc\u30bf\u306b\u5408\u308f\u305b\u3059\u304e\u3066\u3001\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u3067\u7cbe\u5ea6\u304c\u843d\u3061\u308b<\/td>\u5b66\u7fd2\u30c7\u30fc\u30bf\u306e\u7cbe\u5ea6\u306f\u9ad8\u3044\u304c\u3001\u691c\u8a3c\u30c7\u30fc\u30bf\u306e\u7cbe\u5ea6\u304c\u4f4e\u3044<\/td><\/tr>
\u5b66\u7fd2\u4e0d\u8db3<\/td>\u30e2\u30c7\u30eb\u304c\u5358\u7d14\u3059\u304e\u3066\u3001\u30d1\u30bf\u30fc\u30f3\u3092\u6349\u3048\u304d\u308c\u3066\u3044\u306a\u3044<\/td>\u5b66\u7fd2\u30c7\u30fc\u30bf\u3067\u3082\u691c\u8a3c\u30c7\u30fc\u30bf\u3067\u3082\u7cbe\u5ea6\u304c\u4f4e\u3044<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u307b\u307c\u3059\u3079\u3066\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u3001\u300c\u30e2\u30c7\u30eb\u3092\u8907\u96d1\u306b\u3059\u308b\u65b9\u5411\u300d\u304b\u300c\u8907\u96d1\u3055\u3092\u6291\u3048\u308b\u65b9\u5411\u300d\u306e\u3069\u3061\u3089\u304b\u306b\u52b9\u304d\u307e\u3059\u3002\u3053\u306e\u8996\u70b9\u3092\u6301\u3063\u3066\u304a\u304f\u3068\u3001\u5404\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u610f\u5473\u304c\u7406\u89e3\u3057\u3084\u3059\u304f\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

\u30d1\u30e9\u30e1\u30fc\u30bf\u4e00\u89a7<\/h2>\n\n\n\n

\u3053\u306e\u8a18\u4e8b\u3067\u89e3\u8aac\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4e00\u89a7\u3067\u3059\u3002\u540d\u524d\u306f LightGBM \u306e\u30cd\u30a4\u30c6\u30a3\u30d6\u540d\u3092\u57fa\u672c\u3068\u3057\u3001\u30ab\u30c3\u30b3\u5185\u306b scikit-learn API \u3067\u306e\u540d\u524d\u3092\u8a18\u8f09\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n

\u5206\u985e<\/th>LightGBM<\/th>XGBoost<\/th>\u4e0a\u3052\u308b\u3068\u3069\u3046\u306a\u308b\u304b<\/th><\/tr><\/thead>
\u5b66\u7fd2\u306e\u9032\u3081\u65b9<\/td>learning_rate<\/td>eta\uff08learning_rate\uff09<\/td>1\u672c\u3054\u3068\u306e\u4fee\u6b63\u304c\u5927\u304d\u304f\u306a\u308b<\/td><\/tr>
\u5b66\u7fd2\u306e\u9032\u3081\u65b9<\/td>num_iterations\uff08n_estimators\uff09<\/td>num_boost_round\uff08n_estimators\uff09<\/td>\u5b66\u7fd2\u304c\u9032\u3080\u3002\u591a\u3059\u304e\u308b\u3068\u904e\u5b66\u7fd2<\/td><\/tr>
\u6728\u306e\u8907\u96d1\u3055<\/td>num_leaves<\/td>max_leaves<\/td>\u8907\u96d1\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u5bc4\u308a\uff09<\/td><\/tr>
\u6728\u306e\u8907\u96d1\u3055<\/td>max_depth<\/td>max_depth<\/td>\u8907\u96d1\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u5bc4\u308a\uff09<\/td><\/tr>
\u6728\u306e\u8907\u96d1\u3055<\/td>min_data_in_leaf\uff08min_child_samples\uff09<\/td>\u306a\u3057<\/td>\u5358\u7d14\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u3092\u6291\u5236\uff09<\/td><\/tr>
\u6728\u306e\u8907\u96d1\u3055<\/td>min_sum_hessian_in_leaf\uff08min_child_weight\uff09<\/td>min_child_weight<\/td>\u5358\u7d14\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u3092\u6291\u5236\uff09<\/td><\/tr>
\u6728\u306e\u8907\u96d1\u3055<\/td>min_gain_to_split\uff08min_split_gain\uff09<\/td>gamma<\/td>\u5358\u7d14\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u3092\u6291\u5236\uff09<\/td><\/tr>
\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0<\/td>bagging_fraction\uff08subsample\uff09<\/td>subsample<\/td>1.0\u306b\u8fd1\u3065\u304f\u307b\u3069\u30e9\u30f3\u30c0\u30e0\u6027\u304c\u6e1b\u308b<\/td><\/tr>
\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0<\/td>feature_fraction\uff08colsample_bytree\uff09<\/td>colsample_bytree<\/td>1.0\u306b\u8fd1\u3065\u304f\u307b\u3069\u30e9\u30f3\u30c0\u30e0\u6027\u304c\u6e1b\u308b<\/td><\/tr>
\u6b63\u5247\u5316<\/td>lambda_l1\uff08reg_alpha\uff09<\/td>alpha\uff08reg_alpha\uff09<\/td>\u4e88\u6e2c\u304c\u63a7\u3048\u3081\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u3092\u6291\u5236\uff09<\/td><\/tr>
\u6b63\u5247\u5316<\/td>lambda_l2\uff08reg_lambda\uff09<\/td>lambda\uff08reg_lambda\uff09<\/td>\u4e88\u6e2c\u304c\u63a7\u3048\u3081\u306b\u306a\u308b\uff08\u904e\u5b66\u7fd2\u3092\u6291\u5236\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u4ee5\u964d\u3067\u306f\u3001\u5206\u985e\u3054\u3068\u306b\u8a73\u3057\u304f\u898b\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

\u5b66\u7fd2\u306e\u9032\u3081\u65b9<\/h2>\n\n\n\n

learning_rate\uff08\u5b66\u7fd2\u7387\uff09<\/h3>\n\n\n\n

\u52fe\u914d\u30d6\u30fc\u30b9\u30c6\u30a3\u30f3\u30b0\u306f\u3001\u6c7a\u5b9a\u6728\u30921\u672c\u305a\u3064\u8ffd\u52a0\u3057\u306a\u304c\u3089\u4e88\u6e2c\u3092\u4fee\u6b63\u3057\u3066\u3044\u304f\u624b\u6cd5\u3067\u3059\u3002learning_rate \u306f\u3001\u6728\u30921\u672c\u8ffd\u52a0\u3059\u308b\u3054\u3068\u306b\u4e88\u6e2c\u3092\u3069\u308c\u3060\u3051\u4fee\u6b63\u3059\u308b\u304b\u3092\u6c7a\u3081\u307e\u3059\u3002<\/p>\n\n\n\n

\u8a2d\u5b9a<\/th>\u52d5\u304d<\/th><\/tr><\/thead>
\u5c0f\u3055\u304f\u3059\u308b\uff08\u4f8b 0.01\uff09<\/td>\u5c11\u3057\u305a\u3064\u614e\u91cd\u306b\u5b66\u7fd2\u3059\u308b\u3002\u7cbe\u5ea6\u306f\u4e0a\u304c\u308a\u3084\u3059\u3044\u304c\u3001\u6728\u304c\u591a\u304f\u5fc5\u8981\u306b\u306a\u308a\u6642\u9593\u304c\u304b\u304b\u308b<\/td><\/tr>
\u5927\u304d\u304f\u3059\u308b\uff08\u4f8b 0.3\uff09<\/td>\u901f\u304f\u5b66\u7fd2\u3059\u308b\u304c\u3001\u4fee\u6b63\u304c\u7c97\u304f\u306a\u308a\u904e\u5b66\u7fd2\u3057\u3084\u3059\u3044<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u5b9f\u52d9\u3067\u306f 0.01\u301c0.1 \u7a0b\u5ea6\u304c\u3088\u304f\u4f7f\u308f\u308c\u307e\u3059\u3002<\/p>\n\n\n\n

num_iterations \/ n_estimators\uff08\u6728\u306e\u672c\u6570\uff09<\/h3>\n\n\n\n

\u4f5c\u6210\u3059\u308b\u6c7a\u5b9a\u6728\u306e\u672c\u6570\u3067\u3059\u3002learning_rate \u3068\u30bb\u30c3\u30c8\u3067\u8003\u3048\u307e\u3059\u3002learning_rate \u3092\u5c0f\u3055\u304f\u3057\u305f\u5834\u5408\u306f\u3001\u305d\u306e\u5206\u3060\u3051\u6728\u306e\u672c\u6570\u3092\u5897\u3084\u3059\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

    \n
  • \u5897\u3084\u3059\uff1a\u5b66\u7fd2\u304c\u9032\u3080\u304c\u3001\u591a\u3059\u304e\u308b\u3068\u904e\u5b66\u7fd2\u3059\u308b<\/li>\n\n\n\n
  • \u6e1b\u3089\u3059\uff1a\u5b66\u7fd2\u4e0d\u8db3\u306b\u306a\u308b<\/li>\n<\/ul>\n\n\n\n

    early_stopping\uff08\u65e9\u671f\u7d42\u4e86\uff09<\/h3>\n\n\n\n

    \u9069\u5207\u306a\u6728\u306e\u672c\u6570\u3092\u624b\u4f5c\u696d\u3067\u63a2\u3059\u306e\u306f\u624b\u9593\u304c\u304b\u304b\u308a\u307e\u3059\u3002\u305d\u3053\u3067\u5b9f\u52d9\u3067\u306f\u3001\u6728\u306e\u672c\u6570\u3092\u5927\u304d\u3081\uff08\u4f8b 5000\uff09\u306b\u8a2d\u5b9a\u3057\u305f\u3046\u3048\u3067\u3001early_stopping \u3092\u4f7f\u3046\u306e\u304c\u5b9a\u756a\u3067\u3059\u3002<\/p>\n\n\n\n

    early_stopping \u306f\u3001\u691c\u8a3c\u30c7\u30fc\u30bf\u306e\u30b9\u30b3\u30a2\u304c\u4e00\u5b9a\u56de\u6570\uff08\u4f8b 100\u56de\uff09\u9023\u7d9a\u3067\u6539\u5584\u3057\u306a\u304b\u3063\u305f\u6642\u70b9\u3067\u5b66\u7fd2\u3092\u6b62\u3081\u3001\u6700\u3082\u826f\u304b\u3063\u305f\u6642\u70b9\u306e\u6728\u306e\u672c\u6570\u3092\u63a1\u7528\u3059\u308b\u4ed5\u7d44\u307f\u3067\u3059\u3002\u6728\u306e\u672c\u6570\u306f\u300c\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u3082\u306e\u300d\u3067\u306f\u306a\u304f\u3001early_stopping \u3067\u300c\u81ea\u52d5\u7684\u306b\u6c7a\u3081\u308b\u3082\u306e\u300d\u3068\u8003\u3048\u308b\u3068\u5206\u304b\u308a\u3084\u3059\u304f\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

    \u6728\u306e\u8907\u96d1\u3055<\/h2>\n\n\n\n

    num_leaves\uff08\u8449\u306e\u6570\uff09<\/h3>\n\n\n\n

    1\u672c\u306e\u6728\u304c\u6301\u3064\u8449\uff08\u6700\u7d42\u7684\u306a\u5206\u5c90\u5148\uff09\u306e\u6570\u306e\u4e0a\u9650\u3067\u3059\u3002LightGBM \u3067\u6700\u3082\u91cd\u8981\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u4e00\u3064\u3067\u3059\u3002<\/p>\n\n\n\n

    \u8a2d\u5b9a<\/th>\u52d5\u304d<\/th><\/tr><\/thead>
    \u5897\u3084\u3059\uff08\u4f8b 127\uff09<\/td>\u7d30\u304b\u3044\u30d1\u30bf\u30fc\u30f3\u307e\u3067\u6349\u3048\u3089\u308c\u308b\u304c\u3001\u904e\u5b66\u7fd2\u3057\u3084\u3059\u3044<\/td><\/tr>
    \u6e1b\u3089\u3059\uff08\u4f8b 15\uff09<\/td>\u5358\u7d14\u306a\u30e2\u30c7\u30eb\u306b\u306a\u308a\u6c4e\u5316\u3057\u3084\u3059\u3044\u304c\u3001\u5b66\u7fd2\u4e0d\u8db3\u306e\u53ef\u80fd\u6027\u304c\u3042\u308b<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    LightGBM \u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u306f 31 \u3067\u3059\u3002<\/p>\n\n\n\n

    max_depth\uff08\u6728\u306e\u6df1\u3055\uff09<\/h3>\n\n\n\n

    \u6728\u306e\u5206\u5c90\u306e\u6bb5\u6570\u306e\u4e0a\u9650\u3067\u3059\u3002num_leaves \u3068\u5f79\u5272\u304c\u8fd1\u304f\u3001\u6df1\u304f\u3059\u308b\u307b\u3069\u8907\u96d1\u306a\u30e2\u30c7\u30eb\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

      \n
    • LightGBM<\/strong>\uff1a\u30c7\u30d5\u30a9\u30eb\u30c8\u306f -1\uff08\u7121\u5236\u9650\uff09\u3002num_leaves \u3067\u8907\u96d1\u3055\u3092\u5236\u5fa1\u3059\u308b\u3053\u3068\u304c\u591a\u3044<\/li>\n\n\n\n
    • XGBoost<\/strong>\uff1a\u30c7\u30d5\u30a9\u30eb\u30c8\u306f 6\u3002XGBoost \u3067\u306f\u3053\u3061\u3089\u304c\u4e3b\u306a\u8abf\u6574\u5bfe\u8c61<\/li>\n<\/ul>\n\n\n\n

      \u306a\u305c\u4e3b\u306a\u8abf\u6574\u5bfe\u8c61\u304c\u7570\u306a\u308b\u306e\u304b\u306f\u3001\u300cLightGBM \u3068 XGBoost \u306e\u9055\u3044\u300d\u3067\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

      min_data_in_leaf \/ min_child_samples\uff08\u8449\u306e\u6700\u5c0f\u30c7\u30fc\u30bf\u6570\uff09<\/h3>\n\n\n\n

      1\u3064\u306e\u8449\u306b\u6700\u4f4e\u4f55\u4ef6\u306e\u30c7\u30fc\u30bf\u304c\u5fc5\u8981\u304b\u3092\u6c7a\u3081\u307e\u3059\u3002<\/p>\n\n\n\n

        \n
      • \u5927\u304d\u304f\u3059\u308b\uff08\u4f8b 50\u301c100\uff09\uff1a\u5c11\u6570\u306e\u7279\u6b8a\u306a\u30c7\u30fc\u30bf\u306b\u5f15\u3063\u5f35\u3089\u308c\u306b\u304f\u304f\u306a\u308a\u3001\u904e\u5b66\u7fd2\u3092\u6291\u3048\u308b<\/li>\n\n\n\n
      • \u5c0f\u3055\u304f\u3059\u308b\uff1a\u6570\u4ef6\u3057\u304b\u306a\u3044\u4f8b\u5916\u7684\u306a\u30c7\u30fc\u30bf\u307e\u3067\u899a\u3048\u3066\u3057\u307e\u3046<\/li>\n<\/ul>\n\n\n\n

        LightGBM \u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u306f 20 \u3067\u3059\u3002\u30c7\u30fc\u30bf\u4ef6\u6570\u304c\u5c11\u306a\u3044\u5834\u5408\u306b\u7279\u306b\u52b9\u679c\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

        min_child_weight\uff08\u8449\u306e\u6700\u5c0f\u91cd\u307f\uff09<\/h3>\n\n\n\n

        \u8449\u306b\u5fc5\u8981\u306a\u300c\u91cd\u307f\u300d\u306e\u5408\u8a08\u306e\u6700\u5c0f\u5024\u3067\u3059\u3002\u56de\u5e30\u3067\u4e8c\u4e57\u8aa4\u5dee\u3092\u4f7f\u3046\u5834\u5408\u306f\u3001\u5404\u30c7\u30fc\u30bf\u306e\u91cd\u307f\u304c1\u306b\u306a\u308b\u305f\u3081\u3001\u5b9f\u8cea\u7684\u306b\u300c\u8449\u306e\u6700\u5c0f\u30c7\u30fc\u30bf\u6570\u300d\u3068\u540c\u3058\u610f\u5473\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

          \n
        • \u5927\u304d\u304f\u3059\u308b\uff1a\u904e\u5b66\u7fd2\u3092\u6291\u3048\u308b<\/li>\n\n\n\n
        • \u30c7\u30d5\u30a9\u30eb\u30c8\uff1aLightGBM \u306f 0.001\u3001XGBoost \u306f 1<\/li>\n<\/ul>\n\n\n\n

          min_gain_to_split \/ gamma\uff08\u5206\u5c90\u306b\u5fc5\u8981\u306a\u6700\u5c0f\u6539\u5584\u91cf\uff09<\/h3>\n\n\n\n

          \u5206\u5c90\u306b\u3088\u3063\u3066\u8aa4\u5dee\u304c\u3069\u308c\u3060\u3051\u6e1b\u308b\u304b\u3092\u8a08\u7b97\u3057\u3001\u305d\u306e\u6539\u5584\u91cf\u304c\u3053\u306e\u5024\u306b\u6e80\u305f\u306a\u3044\u5834\u5408\u306f\u5206\u5c90\u3057\u307e\u305b\u3093\u3002<\/p>\n\n\n\n

            \n
          • \u5927\u304d\u304f\u3059\u308b\uff1a\u52b9\u679c\u306e\u8584\u3044\u5206\u5c90\u3092\u3057\u306a\u304f\u306a\u308a\u3001\u6728\u304c\u5358\u7d14\u306b\u306a\u308b<\/li>\n\n\n\n
          • \u30c7\u30d5\u30a9\u30eb\u30c8\uff1a\u3069\u3061\u3089\u3082 0\uff08\u6539\u5584\u304c\u3042\u308c\u3070\u5206\u5c90\u3059\u308b\uff09<\/li>\n<\/ul>\n\n\n\n

            \u30e9\u30f3\u30c0\u30e0\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0<\/h2>\n\n\n\n

            bagging_fraction \/ subsample\uff08\u884c\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\uff09<\/h3>\n\n\n\n

            \u6728\u30921\u672c\u4f5c\u308b\u3054\u3068\u306b\u3001\u5b66\u7fd2\u30c7\u30fc\u30bf\u306e\u4f55\u5272\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u4f7f\u3046\u304b\u3092\u6c7a\u3081\u307e\u3059\u3002<\/p>\n\n\n\n

              \n
            • \u4e0b\u3052\u308b\uff08\u4f8b 0.7\u301c0.8\uff09\uff1a\u6bce\u56de\u5c11\u3057\u305a\u3064\u9055\u3046\u30c7\u30fc\u30bf\u3067\u5b66\u7fd2\u3059\u308b\u305f\u3081\u3001\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u3078\u306e\u4f9d\u5b58\u304c\u6e1b\u308a\u3001\u904e\u5b66\u7fd2\u3092\u6291\u3048\u308b<\/li>\n\n\n\n
            • 1.0\uff1a\u6bce\u56de\u3059\u3079\u3066\u306e\u30c7\u30fc\u30bf\u3092\u4f7f\u3046\uff08\u30c7\u30d5\u30a9\u30eb\u30c8\uff09<\/li>\n<\/ul>\n\n\n\n

              \u306a\u304a\u3001LightGBM \u3067\u306f bagging_freq \u30921\u4ee5\u4e0a\u306b\u3057\u306a\u3044\u3068 bagging_fraction \u304c\u6709\u52b9\u306b\u306a\u308a\u307e\u305b\u3093\uff08bagging_freq \u306e\u30c7\u30d5\u30a9\u30eb\u30c8\u306f 0\uff09\u3002XGBoost \u306b\u306f\u3053\u306e\u8a2d\u5b9a\u306f\u306a\u304f\u3001subsample \u3060\u3051\u3067\u6709\u52b9\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

              feature_fraction \/ colsample_bytree\uff08\u7279\u5fb4\u91cf\u306e\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\uff09<\/h3>\n\n\n\n

              \u6728\u30921\u672c\u4f5c\u308b\u3054\u3068\u306b\u3001\u7279\u5fb4\u91cf\uff08\u66dc\u65e5\u30fb\u5929\u6c17\u30fb\u795d\u65e5\u30d5\u30e9\u30b0\u306a\u3069\uff09\u306e\u4f55\u5272\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u4f7f\u3046\u304b\u3092\u6c7a\u3081\u307e\u3059\u3002<\/p>\n\n\n\n

                \n
              • \u4e0b\u3052\u308b\uff08\u4f8b 0.6\u301c0.8\uff09\uff1a\u7279\u5b9a\u306e\u7279\u5fb4\u91cf\u306b\u983c\u308a\u3059\u304e\u308b\u3053\u3068\u3092\u9632\u304e\u3001\u904e\u5b66\u7fd2\u3092\u6291\u3048\u308b<\/li>\n\n\n\n
              • 1.0\uff1a\u6bce\u56de\u3059\u3079\u3066\u306e\u7279\u5fb4\u91cf\u3092\u4f7f\u3046\uff08\u30c7\u30d5\u30a9\u30eb\u30c8\uff09<\/li>\n<\/ul>\n\n\n\n

                \u6b63\u5247\u5316\uff08L1\u30fbL2\uff09<\/h2>\n\n\n\n

                \u6b63\u5247\u5316\u306f\u3001\u8a00\u8449\u3060\u3051\u3067\u306f\u5206\u304b\u308a\u306b\u304f\u3044\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u3059\u3002\u3053\u3053\u3067\u306f\u5177\u4f53\u7684\u306a\u6570\u5024\u3067\u8aac\u660e\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

                \u524d\u63d0\uff1a\u8449\u306e\u5024\u306f\u3069\u3046\u6c7a\u307e\u308b\u304b<\/h3>\n\n\n\n

                \u52fe\u914d\u30d6\u30fc\u30b9\u30c6\u30a3\u30f3\u30b0\u3067\u306f\u3001\u6728\u30921\u672c\u8ffd\u52a0\u3059\u308b\u305f\u3073\u306b\u3001\u5404\u8449\u306b\u5165\u3063\u305f\u30c7\u30fc\u30bf\u306e\u8aa4\u5dee\uff08\u4e88\u6e2c\u306e\u30ba\u30ec\uff09\u3092\u3082\u3068\u306b\u3001\u305d\u306e\u8449\u306e\u88dc\u6b63\u5024\u3092\u6c7a\u3081\u307e\u3059\u3002<\/p>\n\n\n\n

                \u305f\u3068\u3048\u3070\u5ba2\u6570\u3092\u4e88\u6e2c\u3057\u3066\u3044\u3066\u3001\u3042\u308b\u8449\u306b\u30c7\u30fc\u30bf\u304c3\u4ef6\u5165\u308a\u30013\u4ef6\u3068\u3082\u4e88\u6e2c\u3088\u308a\u5b9f\u969b\u306e\u5ba2\u6570\u304c30\u4eba\u591a\u304b\u3063\u305f\u3068\u3057\u307e\u3059\u3002\u6b63\u5247\u5316\u304c\u306a\u3044\u5834\u5408\u3001\u88dc\u6b63\u5024\u306f\u8aa4\u5dee\u306e\u5e73\u5747\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

                $$
                \u88dc\u6b63\u5024 = \\frac{\u8aa4\u5dee\u306e\u5408\u8a08}{\u4ef6\u6570} = \\frac{90}{3} = +30
                $$<\/p>\n\n\n\n

                \u6b63\u5247\u5316\u306f\u3001\u3053\u306e\u8a08\u7b97\u5f0f\u306b\u624b\u3092\u52a0\u3048\u3066\u88dc\u6b63\u3092\u63a7\u3048\u3081\u306b\u3059\u308b\u4ed5\u7d44\u307f\u3067\u3059\u3002<\/p>\n\n\n\n

                L2\u6b63\u5247\u5316\uff08lambda_l2 \/ reg_lambda\uff09<\/h3>\n\n\n\n

                L2\u6b63\u5247\u5316\u306f\u3001\u8a08\u7b97\u5f0f\u306e\u5206\u6bcd\u306b \u03bb \u3092\u8db3\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

                $$
                \u88dc\u6b63\u5024 = \\frac{\u8aa4\u5dee\u306e\u5408\u8a08}{\u4ef6\u6570 + \\lambda}
                $$<\/p>\n\n\n\n

                \u30b1\u30fc\u30b9<\/th>\u03bb = 0<\/th>\u03bb = 3<\/th><\/tr><\/thead>
                \u8449\u306b3\u4ef6\uff08\u8aa4\u5dee\u5408\u8a08 90\uff09<\/td>90 \u00f7 3 = +30<\/td>90 \u00f7 6 = +15\uff08\u534a\u5206\u306b\u306a\u308b\uff09<\/td><\/tr>
                \u8449\u306b300\u4ef6\uff08\u8aa4\u5dee\u5408\u8a08 9000\uff09<\/td>9000 \u00f7 300 = +30<\/td>9000 \u00f7 303 \u2252 +29.7\uff08\u307b\u307c\u5909\u308f\u3089\u306a\u3044\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                \u540c\u3058 \u03bb \u3067\u3082\u3001\u30c7\u30fc\u30bf\u304c\u5c11\u306a\u3044\u8449\u307b\u3069\u5f37\u304f\u6291\u3048\u3089\u308c\u3001\u30c7\u30fc\u30bf\u304c\u591a\u3044\u8449\u306f\u307b\u3068\u3093\u3069\u5f71\u97ff\u3092\u53d7\u3051\u307e\u305b\u3093\u3002\u5076\u7136\u6570\u4ef6\u3060\u3051\u5916\u308c\u305f\u30c7\u30fc\u30bf\u306b\u904e\u5270\u53cd\u5fdc\u3059\u308b\u3053\u3068\u3092\u9632\u304e\u307e\u3059\u3002<\/p>\n\n\n\n

                L1\u6b63\u5247\u5316\uff08lambda_l1 \/ reg_alpha\uff09<\/h3>\n\n\n\n

                L1\u6b63\u5247\u5316\u306f\u3001\u8aa4\u5dee\u306e\u5408\u8a08\u304b\u3089 \u03b1 \u3092\u5dee\u3057\u5f15\u304d\u307e\u3059\u3002\u5dee\u3057\u5f15\u3044\u305f\u7d50\u679c\u304c0\u4ee5\u4e0b\u306b\u306a\u308b\u5834\u5408\u3001\u88dc\u6b63\u5024\u306f0\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

                \u03b1<\/th>\u8a08\u7b97<\/th>\u88dc\u6b63\u5024<\/th><\/tr><\/thead>
                0<\/td>90 \u00f7 3<\/td>+30<\/td><\/tr>
                60<\/td>(90 \u2212 60) \u00f7 3<\/td>+10<\/td><\/tr>
                100<\/td>90 \u304c \u03b1 \u3092\u4e0b\u56de\u308b<\/td>0\uff08\u3053\u306e\u8449\u306f\u4f55\u3082\u88dc\u6b63\u3057\u306a\u3044\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                \u5c0f\u3055\u306a\u30ba\u30ec\u306f\u307e\u308b\u3054\u3068\u7121\u8996\u3057\u3001\u6839\u62e0\u304c\u5341\u5206\u306b\u5927\u304d\u3044\u3068\u304d\u3060\u3051\u88dc\u6b63\u3059\u308b\u306e\u304c L1 \u6b63\u5247\u5316\u3067\u3059\u3002\u88dc\u6b63\u5024\u30920\u306b\u3067\u304d\u308b\u70b9\u304c L2 \u3068\u306e\u9055\u3044\u3067\u3059\u3002<\/p>\n\n\n\n

                \u306a\u304a\u3001\u5b9f\u969b\u306e\u88dc\u6b63\u5024\u306b\u306f\u3055\u3089\u306b learning_rate \u304c\u639b\u3051\u3089\u308c\u307e\u3059\u3002<\/p>\n\n\n\n

                <\/th>L2<\/th>L1<\/th><\/tr><\/thead>
                \u52b9\u304d\u65b9<\/td>\u88dc\u6b63\u5024\u3092\u5272\u5408\u3067\u7e2e\u3081\u308b<\/td>\u5c0f\u3055\u306a\u88dc\u6b63\u30920\u306b\u3059\u308b<\/td><\/tr>
                \u5927\u304d\u304f\u3059\u308b\u3068<\/td>\u5c11\u6570\u30c7\u30fc\u30bf\u306e\u8449\u304c\u63a7\u3048\u3081\u306b\u306a\u308b<\/td>\u52b9\u679c\u306e\u5c0f\u3055\u3044\u8449\u304c\u7121\u52b9\u306b\u306a\u308b<\/td><\/tr>
                \u30c7\u30d5\u30a9\u30eb\u30c8<\/td>LightGBM\uff1a0\u3001XGBoost\uff1a1<\/td>\u3069\u3061\u3089\u3082 0<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                XGBoost \u306f\u30c7\u30d5\u30a9\u30eb\u30c8\u3067 L2 \u6b63\u5247\u5316\u304c\u52b9\u3044\u3066\u3044\u308b\u70b9\u306b\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n\n\n\n

                \u76ee\u7684\u95a2\u6570\u3068\u518d\u73fe\u6027<\/h2>\n\n\n\n

                objective\uff08\u76ee\u7684\u95a2\u6570\uff09<\/h3>\n\n\n\n

                \u30e2\u30c7\u30eb\u304c\u4f55\u3092\u6700\u5c0f\u5316\u3059\u308b\u3088\u3046\u306b\u5b66\u7fd2\u3059\u308b\u304b\u3092\u6c7a\u3081\u307e\u3059\u3002\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u5bfe\u8c61\u3068\u3044\u3046\u3088\u308a\u3001\u4e88\u6e2c\u3057\u305f\u3044\u3082\u306e\u306b\u5408\u308f\u305b\u3066\u9078\u3076\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u3059\u3002<\/p>\n\n\n\n

                \u5185\u5bb9<\/th>LightGBM<\/th>XGBoost<\/th>\u7279\u5fb4<\/th><\/tr><\/thead>
                \u4e8c\u4e57\u8aa4\u5dee<\/td>regression<\/td>reg:squarederror<\/td>\u6a19\u6e96\u3002\u5916\u308c\u5024\u306b\u5f15\u3063\u5f35\u3089\u308c\u3084\u3059\u3044<\/td><\/tr>
                \u7d76\u5bfe\u8aa4\u5dee<\/td>regression_l1<\/td>reg:absoluteerror<\/td>\u5916\u308c\u5024\u306b\u5f37\u3044<\/td><\/tr>
                Huber \u7cfb<\/td>huber<\/td>reg:pseudohubererror<\/td>\u4e8c\u4e57\u8aa4\u5dee\u3068\u7d76\u5bfe\u8aa4\u5dee\u306e\u4e2d\u9593<\/td><\/tr>
                \u30dd\u30a2\u30bd\u30f3<\/td>poisson<\/td>count:poisson<\/td>0\u4ee5\u4e0a\u306e\u30ab\u30a6\u30f3\u30c8\u30c7\u30fc\u30bf\uff08\u5ba2\u6570\u30fb\u4ef6\u6570\u306a\u3069\uff09\u5411\u3051<\/td><\/tr>
                Tweedie<\/td>tweedie<\/td>reg:tweedie<\/td>0\u304c\u591a\u304f\u542b\u307e\u308c\u308b\u30c7\u30fc\u30bf\u5411\u3051<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                seed\uff08\u4e71\u6570\u306e\u7a2e\uff09<\/h3>\n\n\n\n

                \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306a\u3069\u3067\u4f7f\u308f\u308c\u308b\u4e71\u6570\u3092\u56fa\u5b9a\u3059\u308b\u305f\u3081\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3067\u3059\u3002<\/p>\n\n\n\n

                  \n
                • \u56fa\u5b9a\u3059\u308b\u3068\u3001\u4f55\u5ea6\u5b9f\u884c\u3057\u3066\u3082\u540c\u3058\u7d50\u679c\u306b\u306a\u308b\u3002\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5909\u3048\u305f\u3068\u304d\u306e\u52b9\u679c\u3092\u6b63\u3057\u304f\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306b\u5fc5\u9808<\/li>\n\n\n\n
                • seed \u3092\u5909\u3048\u305f\u3060\u3051\u3067\u7d50\u679c\u304c\u5927\u304d\u304f\u5909\u308f\u308b\u5834\u5408\u306f\u3001\u30e2\u30c7\u30eb\u304c\u4e0d\u5b89\u5b9a\u306a\u30b5\u30a4\u30f3<\/li>\n\n\n\n
                • bagging_fraction \u3084 feature_fraction \u304c 1.0 \u306e\u307e\u307e\u306e\u5834\u5408\u306f\u3001seed \u306e\u5f71\u97ff\u306f\u5c0f\u3055\u3044<\/li>\n<\/ul>\n\n\n\n

                  LightGBM \u3067\u306f seed \u304b\u3089\u500b\u5225\u306e seed\uff08bagging_seed\u3001feature_fraction_seed \u306a\u3069\uff09\u304c\u751f\u6210\u3055\u308c\u307e\u3059\u3002\u500b\u5225\u306e seed \u3092\u660e\u793a\u3057\u305f\u5834\u5408\u306f\u305d\u3061\u3089\u304c\u512a\u5148\u3055\u308c\u307e\u3059\u3002\u307e\u305f\u3001\u30de\u30eb\u30c1\u30b9\u30ec\u30c3\u30c9\u5b9f\u884c\u3067\u7d50\u679c\u3092\u5b8c\u5168\u306b\u4e00\u81f4\u3055\u305b\u305f\u3044\u5834\u5408\u306f\u3001deterministic=true \u3068 force_row_wise\uff08\u307e\u305f\u306f force_col_wise\uff09\u3092\u4f75\u7528\u3057\u307e\u3059\u3002<\/p>\n\n\n\n

                  LightGBM \u3068 XGBoost \u306e\u9055\u3044<\/h2>\n\n\n\n

                  \u8003\u3048\u65b9\u306f\u5171\u901a\u3067\u3059\u304c\u30012\u3064\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u3067\u306f\u6728\u306e\u80b2\u3066\u65b9\u3068\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u304c\u7570\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

                  \u6728\u306e\u80b2\u3066\u65b9<\/h3>\n\n\n\n
                  <\/th>LightGBM<\/th>XGBoost<\/th><\/tr><\/thead>
                  \u6210\u9577\u65b9\u5f0f<\/td>leaf-wise\uff08\u8aa4\u5dee\u304c\u6700\u3082\u6e1b\u308b\u8449\u304b\u3089\u512a\u5148\u7684\u306b\u5206\u5c90\uff09<\/td>depth-wise\uff08\u540c\u3058\u6df1\u3055\u306e\u6bb5\u3092\u63c3\u3048\u3066\u5206\u5c90\uff09<\/td><\/tr>
                  \u4e3b\u306a\u8abf\u6574\u5bfe\u8c61<\/td>num_leaves<\/td>max_depth<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                  LightGBM \u306f\u6df1\u3055\u3092\u63c3\u3048\u305a\u306b\u6210\u9577\u3059\u308b\u305f\u3081\u3001\u6728\u306e\u5927\u304d\u3055\u3092\u8449\u306e\u6570\u3067\u5236\u5fa1\u3057\u307e\u3059\u3002\u4e00\u65b9\u3001XGBoost \u306f\u6bb5\u3054\u3068\u306b\u6210\u9577\u3059\u308b\u305f\u3081\u3001\u6df1\u3055\u3067\u5236\u5fa1\u3059\u308b\u306e\u304c\u57fa\u672c\u3067\u3059\u3002\u306a\u304a\u3001XGBoost \u3067\u3082 grow_policy=lossguide \u3092\u6307\u5b9a\u3059\u308b\u3068\u3001LightGBM \u306b\u8fd1\u3044\u6210\u9577\u65b9\u5f0f\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n\n\n\n

                  \u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306e\u9055\u3044<\/h3>\n\n\n\n
                  \u30d1\u30e9\u30e1\u30fc\u30bf<\/th>LightGBM<\/th>XGBoost<\/th><\/tr><\/thead>
                  \u5b66\u7fd2\u7387<\/td>0.1<\/td>0.3<\/td><\/tr>
                  \u6728\u306e\u6df1\u3055<\/td>-1\uff08\u7121\u5236\u9650\uff09<\/td>6<\/td><\/tr>
                  \u8449\u306e\u6570<\/td>31<\/td>0\uff08\u7121\u5236\u9650\uff09<\/td><\/tr>
                  \u8449\u306e\u6700\u5c0f\u91cd\u307f<\/td>0.001<\/td>1<\/td><\/tr>
                  L2\u6b63\u5247\u5316<\/td>0<\/td>1<\/td><\/tr>
                  \u6570\u5024\u306e\u533a\u5207\u308a\u6570\uff08max_bin\uff09<\/td>255<\/td>256<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                  \u540c\u3058\u30c7\u30fc\u30bf\u3092\u300c\u30c7\u30d5\u30a9\u30eb\u30c8\u8a2d\u5b9a\u300d\u3067\u5b66\u7fd2\u3055\u305b\u3066\u3082\u30012\u3064\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u7d50\u679c\u306f\u4e00\u81f4\u3057\u307e\u305b\u3093\u3002\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u79fb\u690d\u3059\u308b\u5834\u5408\u306f\u3001\u540d\u524d\u3060\u3051\u3067\u306a\u304f\u30c7\u30d5\u30a9\u30eb\u30c8\u5024\u306e\u9055\u3044\u306b\u3082\u6ce8\u610f\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n\n\n\n

                  \u5b9f\u52d9\u3067\u306e\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u624b\u9806<\/h2>\n\n\n\n

                  \u3059\u3079\u3066\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u540c\u6642\u306b\u8abf\u6574\u3059\u308b\u3068\u3001\u4f55\u304c\u52b9\u3044\u305f\u306e\u304b\u5206\u304b\u3089\u306a\u304f\u306a\u308a\u307e\u3059\u3002\u5f71\u97ff\u306e\u5927\u304d\u3044\u3082\u306e\u304b\u3089\u9806\u306b\u8abf\u6574\u3059\u308b\u306e\u304c\u304a\u3059\u3059\u3081\u3067\u3059\u3002<\/p>\n\n\n\n

                    \n
                  1. seed \u3092\u56fa\u5b9a\u3057\u3001learning_rate \u3092 0.05 \u7a0b\u5ea6\u306b\u3057\u3066\u3001early_stopping \u3067\u6728\u306e\u672c\u6570\u3092\u6c7a\u3081\u308b<\/li>\n\n\n\n
                  2. \u6728\u306e\u8907\u96d1\u3055\uff08LightGBM \u306f num_leaves\u3001XGBoost \u306f max_depth\uff09\u3068\u3001\u8449\u306e\u6700\u5c0f\u30c7\u30fc\u30bf\u6570\u3092\u8abf\u6574\u3059\u308b<\/li>\n\n\n\n
                  3. \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\uff08bagging_fraction \/ subsample\u3001feature_fraction \/ colsample_bytree\uff09\u3092\u8abf\u6574\u3059\u308b<\/li>\n\n\n\n
                  4. \u5fc5\u8981\u306b\u5fdc\u3058\u3066\u6b63\u5247\u5316\uff08L1\u30fbL2\uff09\u3092\u8abf\u6574\u3059\u308b<\/li>\n\n\n\n
                  5. \u6700\u5f8c\u306b learning_rate \u3092\u4e0b\u3052\u3066\uff08\u4f8b 0.01\uff09\u4ed5\u4e0a\u3052\u308b<\/li>\n<\/ol>\n\n\n\n

                    \u624b\u4f5c\u696d\u3067\u8a66\u3059\u306e\u304c\u5927\u5909\u306a\u5834\u5408\u306f\u3001Optuna \u306a\u3069\u306e\u30c4\u30fc\u30eb\u3067\u81ea\u52d5\u63a2\u7d22\u3059\u308b\u306e\u3082\u6709\u52b9\u3067\u3059\u3002\u305d\u306e\u5834\u5408\u3082\u3001\u3053\u306e\u8a18\u4e8b\u3067\u7d39\u4ecb\u3057\u305f\u5404\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u610f\u5473\u3092\u7406\u89e3\u3057\u3066\u304a\u304f\u3068\u3001\u63a2\u7d22\u7bc4\u56f2\u3092\u9069\u5207\u306b\u8a2d\u5b9a\u3067\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

                    \u307e\u3068\u3081<\/h2>\n\n\n\n

                    \u30d1\u30e9\u30e1\u30fc\u30bf\u306f\u6570\u304c\u591a\u3044\u3067\u3059\u304c\u3001\u307b\u3068\u3093\u3069\u306f\u300c\u30e2\u30c7\u30eb\u3092\u8907\u96d1\u306b\u3059\u308b\u304b\u3001\u6291\u3048\u308b\u304b\u300d\u306e\u3069\u3061\u3089\u304b\u306b\u52b9\u304d\u307e\u3059\u3002<\/p>\n\n\n\n

                    \u76ee\u7684<\/th>\u8abf\u6574\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf<\/th><\/tr><\/thead>
                    \u5b66\u7fd2\u306e\u901f\u3055\u3068\u7cbe\u5ea6\u306e\u30d0\u30e9\u30f3\u30b9<\/td>learning_rate \u3068\u6728\u306e\u672c\u6570\uff08early_stopping \u3067\u6c7a\u3081\u308b\uff09<\/td><\/tr>
                    \u6728\u306e\u8907\u96d1\u3055<\/td>num_leaves\uff08LightGBM\uff09\uff0fmax_depth\uff08XGBoost\uff09<\/td><\/tr>
                    \u5c11\u6570\u30c7\u30fc\u30bf\u3078\u306e\u904e\u5270\u53cd\u5fdc\u3092\u9632\u3050<\/td>min_data_in_leaf\u3001L2\u6b63\u5247\u5316<\/td><\/tr>
                    \u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u30fb\u7279\u5fb4\u91cf\u3078\u306e\u4f9d\u5b58\u3092\u9632\u3050<\/td>bagging_fraction\u3001feature_fraction<\/td><\/tr>
                    \u7d50\u679c\u306e\u518d\u73fe\u6027<\/td>seed<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                    \u904e\u5b66\u7fd2\u3057\u3066\u3044\u308b\u3068\u611f\u3058\u305f\u3089\u300c\u6291\u3048\u308b\u65b9\u5411\u300d\u3001\u5b66\u7fd2\u4e0d\u8db3\u306a\u3089\u300c\u8907\u96d1\u306b\u3059\u308b\u65b9\u5411\u300d\u3078\u8abf\u6574\u3059\u308b\u3001\u3068\u3044\u3046\u57fa\u672c\u3092\u62bc\u3055\u3048\u3066\u304a\u3051\u3070\u3001\u3069\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u3069\u3061\u3089\u306b\u52d5\u304b\u3059\u3079\u304d\u304b\u304c\u5224\u65ad\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

                    \u306f\u3058\u3081\u306b LightGBM \u3084 XGBoost \u3067\u30e2\u30c7\u30eb\u3092\u4f5c\u308b\u3068\u304d\u3001learning_rate \u3084 num_leaves \u306a\u3069\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u300c\u3088\u304f\u4f7f\u308f\u308c\u308b\u5024\u300d\u306e\u307e\u307e\u3001\u610f\u5473\u3092\u5341\u5206\u306b\u7406\u89e3\u3057\u306a\u3044\u307e\u307e\u8a2d\u5b9a\u3057\u3066\u3044\u308b\u30b1\u30fc\u30b9\u306f\u5c11\u306a\u304f\u3042 […]<\/p>\n","protected":false},"author":69,"featured_media":10303,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","swell_btn_cv_data":"","footnotes":"","_wp_rev_ctl_limit":""},"categories":[1251,1249],"tags":[331,39,27,425,26,419],"class_list":["post-10300","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","category-knowledge","tag-python","tag-39","tag-27","tag-425","tag-26","tag-419"],"_links":{"self":[{"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/posts\/10300","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/comments?post=10300"}],"version-history":[{"count":3,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/posts\/10300\/revisions"}],"predecessor-version":[{"id":10304,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/posts\/10300\/revisions\/10304"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/media\/10303"}],"wp:attachment":[{"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/media?parent=10300"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/categories?post=10300"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/tags?post=10300"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}