{"id":64455,"date":"2021-10-01T10:32:35","date_gmt":"1970-01-01T00:00:00","guid":{"rendered":"https:\/\/www.sejuku.net\/blog\/?p=64455"},"modified":"2024-05-06T11:50:03","modified_gmt":"2024-05-06T02:50:03","slug":"ph8-2","status":"publish","type":"post","link":"https:\/\/www.sejuku.net\/blog\/64455","title":{"rendered":"\u6a5f\u68b0\u5b66\u7fd2\u624b\u6cd5\u300c\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u300d\u3067\u30af\u30e9\u30b9\u5206\u985e\u306b\u30c1\u30e3\u30ec\u30f3\u30b8\u3057\u3088\u3046"},"content":{"rendered":"<p><span style=\"color: #008000;\"><strong>Deep Learning<\/strong><\/span>\u306e\u3088\u3046\u306a\u30d1\u30ef\u30d5\u30eb\u306a\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3082\u3044\u3044\u3067\u3059\u304c\u3001<strong><span style=\"color: #0000ff;\">\u3082\u3063\u3068\u624b\u8efd\u306a\u30e2\u30c7\u30eb\u304c\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059<\/span><\/strong>\u3002<\/p>\n<p><span style=\"color: #008000;\"><strong>Python\u3068scikit-learn<\/strong><\/span>\u3067\u624b\u8efd\u306b\u6a5f\u68b0\u5b66\u7fd2\u3092\u4f53\u9a13\u3057\u3061\u3083\u3044\u307e\u3057\u3087\u3046\uff01<\/p>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u7d39\u4ecb\u3059\u308b\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306f<strong><span style=\"color: #008000;\">\u300c\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u300d<\/span><\/strong>\u3067\u3059\u3002<\/p>\n<div style=\"padding: 10px 5px 10px 20px; margin-bottom: 20px; border: 2px solid #4286f4;\">\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u308f\u304b\u308b\u3053\u3068<\/p>\n<ul>\n<li><span style=\"color: #0000ff;\"><strong>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3068\u306f<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u4f7f\u3044\u65b9<\/strong><\/span><\/li>\n<li><span style=\"color: #0000ff;\"><strong>\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u65b9\u6cd5<\/strong><\/span><\/li>\n<\/ul>\n<\/div>\n<p>\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u4e00\u3064\u306b<span style=\"color: #008000;\"><strong>\u300c\u6c7a\u5b9a\u6728\u300d<\/strong><\/span>\u3068\u3044\u3046\u3082\u306e\u304c\u3042\u308a\u307e\u3059\u304c\u3001\u3053\u308c\u3092\u4f7f\u3063\u305f<span style=\"color: #008000;\"><strong>\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306e\u4e00\u3064<\/strong><\/span>\u3068\u3057\u3066\u6709\u540d\u306a<strong><span style=\"color: #008000;\">\u300c\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u300d<\/span><\/strong>\u306b\u3064\u3044\u3066\u306f\u5e83\u304f\u4f7f\u308f\u308c\u308b\u6709\u540d\u306a\u30e2\u30c7\u30eb\u3067\u3059\u3002<\/p>\n<p>\u300c\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u300d\u3092\u4f7f\u3044\u3053\u306a\u3057\u3066\u4e00\u6b69\u9032\u3093\u3060\u30c7\u30fc\u30bf\u89e3\u6790\u3092\u884c\u3044\u307e\u3057\u3087\u3046\u3002<\/p>\n<p><span style=\"background-color: #ffcc00;\"><strong>\u3053\u306e\u8a18\u4e8b\u306e\u30b3\u30fc\u30c9\u306fPython 3.6.7\u3001scikit-learn 0.19.2\u3067\u52d5\u4f5c\u78ba\u8a8d\u3057\u307e\u3057\u305f\u3002<\/strong><\/span><\/p>\n<h2>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3068\u306f<\/h2>\n<p>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306f<span style=\"color: #008000;\"><strong>\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb<\/strong><\/span>\u306e\u4e00\u3064\u3067\u3059\u3002<\/p>\n<p><span style=\"color: #008000;\"><strong>\u30af\u30e9\u30b9\u5206\u985e\u3001\u56de\u5e30\u3001\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0<\/strong><\/span>\u306a\u3069\u306b\u5229\u7528\u3059\u308b\u4e8b\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<p><span style=\"color: #0000ff;\"><strong>\u8907\u6570\u306e\u6c7a\u5b9a\u6728\u3092\u4f7f\u3063\u3066\u5404\u6c7a\u5b9a\u6728\u306e\u4e88\u6e2c\u7d50\u679c\u306e\u591a\u6570\u6c7a\u3067\u7d50\u679c\u3092\u6c42\u3081\u308b<\/strong><\/span>\u65b9\u6cd5\u3067\u3059\u3002<\/p>\n<p>\u3053\u306e\u3088\u3046\u306b<span style=\"color: #0000ff;\"><strong>\u8907\u6570\u306e\u30e2\u30c7\u30eb\u3092\u4f7f\u3063\u3066\u6027\u80fd\u3092\u4e0a\u3052\u308b\u5b66\u7fd2\u65b9\u6cd5\u3092\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2<\/strong><\/span>\u3068\u3044\u3044\u3001\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u4ee5\u5916\u306b\u3082\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u3082\u3063\u3068\u8a73\u3057\u304f\u77e5\u308a\u305f\u3044\u65b9\u306f\u3001\u53c2\u8003\u66f8\u3084<a href=\"https:\/\/lp.sejuku.net\/lp1_blog_01\/?cid=ai_btn1_64455\">\u4f8d\u30a8\u30f3\u30b8\u30cb\u30a2\u306e\u30de\u30f3\u30c4\u30fc\u30de\u30f3\u30ec\u30c3\u30b9\u30f3<\/a>\u3092\u8a66\u3057\u3066\u307f\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<h2>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u4f7f\u3044\u65b9<\/h2>\n<h3>\u76ee\u7684<\/h3>\n<p><span style=\"color: #008000;\"><strong>scikit-learn<\/strong><\/span>\u3092\u4f7f\u3063\u3066\u7c21\u5358\u306b\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3092\u8a66\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<p>\u3053\u3053\u3067\u306fscikit-learn\u306e<span style=\"color: #0000ff;\"><strong>digits\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3063\u3066\u30af\u30e9\u30b9\u5206\u985e<\/strong><\/span>\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\n<h3>\u30e9\u30a4\u30d6\u30e9\u30ea\u306eimport<\/h3>\n<p>\u307e\u305a\u306f\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<pre class=\"lang:python decode:true\">from sklearn import datasets\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.ensemble import RandomForestClassifier as RFC\r\n\r\nfrom sklearn.model_selection import train_test_split, GridSearchCV<\/pre>\n<p>\uff14\u884c\u76ee\u306eimport\u304c\u3053\u3053\u3067\u4f7f\u3046\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u30af\u30e9\u30b9\u3067\u3059\u3002<\/p>\n<p>scikit-learn\uff08\u4ee5\u4e0bsklearn\uff09\u306b\u306f<\/p>\n<ul>\n<li><span style=\"color: #008000;\"><strong>RandomForestClassifier<\/strong><\/span>\n<ul>\n<li>\u30af\u30e9\u30b9\u5206\u985e\u7528\uff08\u4eca\u56de\u306f\u3053\u3063\u3061\uff09<\/li>\n<\/ul>\n<\/li>\n<li><span style=\"color: #008000;\"><strong>RandomForestRegressor\u00a0<\/strong><\/span>\n<ul>\n<li>\u56de\u5e30\u5206\u6790\u7528<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>\u306e\u4e8c\u7a2e\u985e\u306e\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30af\u30e9\u30b9\u304c\u3042\u308b\u306e\u3067\u3001\u76ee\u7684\u306b\u3042\u3063\u305f\u3082\u306e\u3092\u4f7f\u3044\u307e\u3057\u3087\u3046\u3002<\/p>\n<h3>\u30c7\u30fc\u30bf\u306e\u8aad\u307f\u8fbc\u307f<\/h3>\n<p>\u30c7\u30fc\u30bf\u3092\u8aad\u307f\u8fbc\u307f\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true\">mnist = datasets.load_digits()\r\n\r\ntrain_images, test_images, train_labels, test_labels = \r\ntrain_test_split(mnist.data, mnist.target, test_size=0.2)\r\n<\/pre>\n<p><span style=\"color: #0000ff;\"><strong>\u8aad\u307f\u8fbc\u307f\u306fdatasets.load_foo()\u95a2\u6570<\/strong><\/span>\u3067\u884c\u3044\u307e\u3059\u3002<\/p>\n<p>\u305d\u306e\u6b21\u306e\u884c\u306f\u8aad\u307f\u8fbc\u3093\u3060\u30c7\u30fc\u30bf\u3092<\/p>\n<ol>\n<li>\u6559\u5e2b\u30c7\u30fc\u30bf<\/li>\n<li>\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf<\/li>\n<li>\u6559\u5e2b\u30e9\u30d9\u30eb<\/li>\n<li>\u30c6\u30b9\u30c8\u30e9\u30d9\u30eb<\/li>\n<\/ol>\n<p>\u306b\u5206\u5272\u3059\u308b\u30b3\u30fc\u30c9\u3067\u3059\u3002<\/p>\n<p>\u3053\u3053\u3067\u3001<span style=\"color: #0000ff;\"><strong>train_test_split\u95a2\u6570\u306etest_size\u306b\u4e0e\u3048\u305f\u5024\u304c\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u5272\u5408<\/strong><\/span>\u306b\u306a\u308b\u306e\u3067\u6ce8\u610f\u3057\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u3053\u3053\u3067\u8aad\u307f\u8fbc\u3093\u3060\u30c7\u30fc\u30bf\u306f8&#215;8\u306e\u30d4\u30af\u30bb\u30eb\u304c\u3042\u308b\u624b\u66f8\u304d\u6587\u5b57\u3067\u3057\u305f\u3002<\/p>\n<p>\u5b9f\u969b\u306b\u8868\u793a\u3057\u3066\u307f\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<pre class=\"lang:python decode:true \">plt.figure(figsize=(15,15))\r\nfor i in range(25):\r\n    plt.subplot(5,5,i+1)\r\n    plt.xticks([])\r\n    plt.yticks([])\r\n    plt.imshow(train_images[i].reshape((8,8)), cmap=plt.cm.binary)\r\n    plt.xlabel(train_labels[i])<\/pre>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-65519\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits.png\" alt=\"\" width=\"854\" height=\"847\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits.png 854w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits-150x150.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits-300x298.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits-640x635.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits-810x803.png 810w\" sizes=\"(max-width: 854px) 100vw, 854px\" \/><\/p>\n<p>\u3053\u3053\u3067\u306f25\u500b\u306e\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\u3057\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n<p><span style=\"color: #0000ff;\"><strong>\u3053\u306e\u753b\u50cf\u3092\u3001\u30e9\u30d9\u30eb\u306e\u6570\u5b57\u3092\u30af\u30e9\u30b9\u3068\u3057\u3066\u30af\u30e9\u30b9\u5206\u985e\u3092\u884c\u3046\u306e\u304c\u76ee\u7684<\/strong><\/span>\u3067\u3059\u3002<\/p>\n<h3>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30e2\u30c7\u30eb\u306e\u5b66\u7fd2<\/h3>\n<p>sklearn\u3067\u306e\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u4f7f\u7528\u306f\u975e\u5e38\u306b\u7c21\u5358\u3067\u3059\u3002<\/p>\n<ol>\n<li>sklearn\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\uff08\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\uff09\u306e<span style=\"color: #0000ff;\"><strong>\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u6210\u3059\u308b<\/strong><\/span><\/li>\n<li>\u6559\u5e2b\u30c7\u30fc\u30bf\u3068\u6559\u5e2b\u30e9\u30d9\u30eb\u3092\u4f7f\u3044\u3001<span style=\"color: #0000ff;\"><strong>fit\u30e1\u30bd\u30c3\u30c9\u3067\u30e2\u30c7\u30eb\u3092\u5b66\u7fd2<\/strong><\/span><\/li>\n<\/ol>\n<pre class=\"lang:python decode:true\">clf = RFC(verbose=True,       # \u5b66\u7fd2\u4e2d\u306b\u30ed\u30b0\u3092\u8868\u793a\u3057\u307e\u3059\u3002\u3053\u306e\u6307\u5b9a\u306f\u306a\u304f\u3066\u3082OK\r\n          n_jobs=-1,          # \u8907\u6570\u306eCPU\u30b3\u30a2\u3092\u4f7f\u3063\u3066\u4e26\u5217\u306b\u5b66\u7fd2\u3057\u307e\u3059\u3002-1\u306f\u6700\u5927\u5024\u3002\r\n          random_state=2525)  # \u4e71\u6570\u306e\u30b7\u30fc\u30c9\u3067\u3059\u3002\r\nclf.fit(train_images, train_labels)<\/pre>\n<p>[\u51fa\u529b\u7d50\u679c]<\/p>\n<pre class=\"lang:default decode:true \">[Parallel(n_jobs=-1)]: Done  10 out of  10 | elapsed:    0.0s finished\r\nRandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\r\n            max_depth=None, max_features='auto', max_leaf_nodes=None,\r\n            min_impurity_decrease=0.0, min_impurity_split=None,\r\n            min_samples_leaf=1, min_samples_split=2,\r\n            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\r\n            oob_score=False, random_state=2525, verbose=True,\r\n            warm_start=False)<\/pre>\n<p>\u3053\u306e\u8a2d\u5b9a\u3060\u3068\u3001\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\uff08\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u308b\u969b\u306e\u5f15\u6570\uff09\u306f\u6700\u4f4e\u9650\u3067\u3001\u307b\u307c\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u307e\u307e\u3067\u884c\u3063\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>fit\u30e1\u30bd\u30c3\u30c9\u3092\u30ad\u30c3\u30af\u3057\u305f\u6bb5\u968e\u3067\u5b66\u7fd2\u304c\u59cb\u307e\u308b\u306e\u3067\u3001\u666e\u901a\u306a\u3089\u3070\u4e00\u77ac\u3067\u5b66\u7fd2\u304c\u7d42\u308f\u308a\u307e\u3059\u3002<\/p>\n<h3>\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u306e\u8a55\u4fa1<\/h3>\n<p>\u5b9f\u969b\u306b\u5b66\u7fd2\u3057\u305f\u30e2\u30c7\u30eb\u306e\u8a55\u4fa1\u3092\u884c\u3044\u307e\u3057\u3087\u3046\u3002<\/p>\n<p>\u307e\u305a\u306f<span style=\"color: #008000;\"><strong>accuracy\uff08\u6b63\u7b54\u7387\uff09<\/strong><\/span>\u3092\u898b\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<p>\u6b63\u7b54\u7387\u306f<\/p>\n<pre class=\"lang:default decode:true\">acc = \uff08\u4e88\u6e2c\u30e9\u30d9\u30eb\u304c\u30c6\u30b9\u30c8\u30e9\u30d9\u30eb\u3068\u540c\u3058\u3060\u3063\u305f\u6570\uff09\/ \uff08\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u6570\uff09<\/pre>\n<p>\u3067\u8a08\u7b97\u3067\u304d\u307e\u3059\u3002<\/p>\n<p>sklearn\u3067\u306f<span style=\"color: #008000;\"><strong>score\u3068\u3044\u3046\u30e1\u30bd\u30c3\u30c9<\/strong><\/span>\u3067\u7c21\u5358\u306b\u8a08\u7b97\u3057\u307e\u3057\u3087\u3046\u3002<\/p>\n<pre class=\"lang:python decode:true\">print(f\"acc: {clf.score(test_images, test_labels)}\")\r\n# acc: 0.9416666666666667\r\n<\/pre>\n<p>\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092\u884c\u3063\u3066\u3044\u306a\u3044\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3067\u3082<strong><span style=\"color: #ff0000;\">94%\u306e\u6b63\u7b54\u7387<\/span><\/strong>\u306b\u306a\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<p>\u307e\u305f\u3001\u4e88\u6e2c\u30e9\u30d9\u30eb\u3060\u3051\u304c\u6b32\u3057\u3044\u5834\u5408\u306f<span style=\"color: #008000;\"><strong>predict\u30e1\u30bd\u30c3\u30c9<\/strong><\/span>\u3092\u4f7f\u3044\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true\">predicted_labels = clf.predict(test_images)<\/pre>\n<p>\u3053\u308c\u3067predicted_labels\u306b\u306ftest_labels\u3068\u540c\u3058\u30b5\u30a4\u30ba\u306e\u914d\u5217\u304c\u4fdd\u5b58\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n<p>\u5b9f\u969b\u306b\u53ef\u8996\u5316\u3057\u3066\u7d50\u679c\u3092\u8a73\u3057\u304f\u898b\u3066\u307f\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true \">plt.figure(figsize=(15,15))\r\n\r\n# \u5148\u982d\u304b\u308925\u679a\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3092\u53ef\u8996\u5316\r\nfor i in range(25):\r\n\r\n    # \u753b\u50cf\u3092\u4f5c\u6210\r\n    plt.subplot(5,5,i+1)\r\n    plt.xticks([])\r\n    plt.yticks([])\r\n    plt.imshow(test_images[i].reshape((8,8)), cmap=plt.cm.binary)\r\n\r\n    # \u4eca\u30d7\u30ed\u30c3\u30c8\u3092\u4f5c\u3063\u3066\u3044\u308b\u753b\u50cf\u30c7\u30fc\u30bf\u306e\u4e88\u6e2c\u30e9\u30d9\u30eb\u3068\u6b63\u89e3\u30e9\u30d9\u30eb\u3092\u30bb\u30c3\u30c8\r\n    predicted_label = predicted_labels[i]\r\n    true_label      = test_labels[i]\r\n\r\n    # \u4e88\u6e2c\u30e9\u30d9\u30eb\u304c\u6b63\u89e3\u306a\u3089\u7dd1\u3001\u4e0d\u6b63\u89e3\u306a\u3089\u8d64\u8272\u3092\u4f7f\u3046\r\n    if predicted_label == true_label:\r\n        color = 'green' # True label color\r\n    else:\r\n        color = 'red'   # False label color\r\n    plt.xlabel(\"{} True({})\".format(predicted_label,\r\n                                  true_label),\r\n                                  color=color)<\/pre>\n<h3><img decoding=\"async\" class=\"aligncenter size-full wp-image-65520\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits2.png\" alt=\"\" width=\"854\" height=\"847\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits2.png 854w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits2-150x150.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits2-300x298.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits2-640x635.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits2-810x803.png 810w\" sizes=\"(max-width: 854px) 100vw, 854px\" \/><\/h3>\n<p>\u3053\u306e\u56f3\u306f\u30c6\u30b9\u30c8\u30c7\u30fc\u30bf\u3068\u4e88\u6e2c\u30e9\u30d9\u30eb\u3001\u305d\u3057\u3066\u30c6\u30b9\u30c8\u30e9\u30d9\u30eb\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u8d64\u8272\u306b\u306a\u3063\u3066\u3044\u308b\u3082\u306e\u306f\u8aa4\u7b54\u3067\u3059\u3002<\/p>\n<p>\u3053\u308c\u3067\u3082\u5341\u5206\u826f\u3044\u6210\u679c\u3060\u3068\u601d\u3044\u307e\u3059\u304c\u3001\u3082\u3046\u5c11\u3057\u6027\u80fd\u3092\u4e0a\u3052\u308b\u65b9\u6cd5\u3092\u8003\u3048\u3066\u307f\u307e\u3057\u3087\u3046\u3002<\/p>\n<h2>\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0<\/h2>\n<h3>\u30b0\u30ea\u30c3\u30c9\u30b5\u30fc\u30c1\u3068\u306f<\/h3>\n<p>\u5148\u7a0b\u306e\u65b9\u6cd5\u3067\u306f\u3001\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e<span style=\"color: #ff0000;\"><strong>\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u5168\u304f\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3057\u307e\u305b\u3093\u3067\u3057\u305f<\/strong><\/span>\u3002<\/p>\n<p>\u3053\u3053\u3067\u306f\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u30c1\u30e5\u30fc\u30cb\u30f3\u30b0\u3092<span style=\"color: #008000;\"><strong>\u30b0\u30ea\u30c3\u30c9\u30b5\u30fc\u30c1<\/strong><\/span>\u3068\u547c\u3070\u308c\u308b\u65b9\u6cd5\u3067\u884c\u3044\u307e\u3059\u3002<\/p>\n<p>\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306a\u3069\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306f\u3001<strong><span style=\"color: #0000ff;\">\u30cf\u30a4\u30d1\u30fc\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u8a2d\u5b9a\u3092\u5909\u3048\u308b\u3068\u6027\u80fd\u304c\u5927\u304d\u304f\u5909\u308f\u308b<\/span><\/strong>\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<p>\u3067\u3059\u304c\u3069\u306e\u3088\u3046\u306a\u8a2d\u5b9a\u306b\u3059\u308c\u3070\u3044\u3044\u306e\u304b\u306f\u3001\u7d4c\u9a13\u5247\u3067\u6c7a\u3081\u308b\u3053\u3068\u304c\u591a\u3044\u3067\u3059\u3002<\/p>\n<p>\u3053\u308c\u3092\u81ea\u52d5\u3067\u63a2\u7d22\u3057\u3066\u3057\u307e\u3046\u65b9\u6cd5\u304c\u3044\u304f\u3064\u304b\u3042\u308a\u307e\u3059\u304c\u3001\u305d\u306e\u4e2d\u3067\u3082\u4e00\u756a\u611a\u76f4\u306a\u65b9\u6cd5\u304c\u30b0\u30ea\u30c3\u30c9\u30b5\u30fc\u30c1\u3067\u3059\u3002<\/p>\n<p>\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u3059\u3079\u3066\u8a66\u3057\u3066\u3001\u4e00\u756a\u6c4e\u5316\u6027\u80fd\u304c\u9ad8\u304b\u3063\u305f\u3082\u306e\u3092\u6700\u3082\u826f\u3044\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u7d44\u307f\u5408\u308f\u305b\u3068\u3057\u3066\u6559\u3048\u3066\u304f\u308c\u307e\u3059\u3002<\/p>\n<h3>\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u63a2\u7d22<\/h3>\n<p>\u307e\u305a\u306f\u63a2\u7d22\u3059\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u7a7a\u9593\u3092\u6c7a\u3081\u307e\u3059\u3002<\/p>\n<p>\u3053\u308c\u306b\u306f\u8f9e\u66f8\u578b\u3092\u7528\u3044\u307e\u3059\u3002<\/p>\n<p>\u3053\u306e\u8f9e\u66f8\u578b\u306ekey\u306f\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u30af\u30e9\u30b9\u306e\u5f15\u6570\u306e\u540d\u524d\u3067\u3001\u5024\u306b\u305d\u306e\u5f15\u6570\u3067\u8a66\u3057\u305f\u3044\u3082\u306e\u3092\u30ea\u30b9\u30c8\u306b\u3057\u3066\u6e21\u3057\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true \">search_params = {\r\n     'n_estimators'      : [5, 10, 20, 30, 50, 100, 300],\r\n      'max_features'      : [3, 5, 10, 15, 20],\r\n      'random_state'      : [2525],\r\n      'n_jobs'            : [1],\r\n      'min_samples_split' : [3, 5, 10, 15, 20, 25, 30, 40, 50, 100],\r\n      'max_depth'         : [3, 5, 10, 15, 20, 25, 30, 40, 50, 100]\r\n}<\/pre>\n<p>key\u306f\u30af\u30e9\u30b9\u306e\u5f15\u6570\u3092\u898b\u306a\u304c\u3089\u6c7a\u3081\u3066\u304f\u3060\u3055\u3044\u3002<\/p>\n<p>\u4f8b\u3048\u3070\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306b\u3064\u3044\u3066\u306e\u77e5\u8b58\u304c\u3042\u308b\u306a\u3089\u3070\u3001\u3082\u3046\u5c11\u3057\u8ce2\u3044\u30d1\u30e9\u30e1\u30fc\u30bf\u63a2\u7d22\u7a7a\u9593\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u306d\u3002<\/p>\n<p>\u3053\u308c\u3092<span style=\"color: #008000;\"><strong>GridSearchCV\u30af\u30e9\u30b9<\/strong><\/span>\u306b\u6e21\u3057\u3066\u63a2\u7d22\u3057\u307e\u3059\u3002<\/p>\n<pre class=\"lang:python decode:true\">gs = GridSearchCV(RFC(),           # \u5bfe\u8c61\u306e\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\r\n                  search_params,   # \u63a2\u7d22\u30d1\u30e9\u30e1\u30bf\u8f9e\u66f8\r\n                  cv=3,            # \u30af\u30ed\u30b9\u30d0\u30ea\u30c7\u30fc\u30b7\u30e7\u30f3\u306e\u5206\u5272\u6570\r\n                  verbose=True,    # \u30ed\u30b0\u8868\u793a\r\n                  n_jobs=-1)       # \u4e26\u5217\u51e6\u7406\r\ngs.fit(train_images, train_labels)\r\n\r\nprint(gs.best_estimator_)<\/pre>\n<p>[\u51fa\u529b\u7d50\u679c]<\/p>\n<pre class=\"lang:default decode:true\">Fitting 3 folds for each of 3500 candidates, totalling 10500 fits\r\n[Parallel(n_jobs=-1)]: Done  68 tasks      | elapsed:    1.8s\r\n[Parallel(n_jobs=-1)]: Done 1225 tasks      | elapsed:   11.4s\r\n[Parallel(n_jobs=-1)]: Done 2225 tasks      | elapsed:   22.8s\r\n[Parallel(n_jobs=-1)]: Done 3325 tasks      | elapsed:   36.5s\r\n[Parallel(n_jobs=-1)]: Done 4225 tasks      | elapsed:   48.4s\r\n[Parallel(n_jobs=-1)]: Done 5325 tasks      | elapsed:  1.0min\r\n[Parallel(n_jobs=-1)]: Done 6625 tasks      | elapsed:  1.3min\r\n[Parallel(n_jobs=-1)]: Done 8125 tasks      | elapsed:  1.6min\r\n[Parallel(n_jobs=-1)]: Done 9825 tasks      | elapsed:  2.0min\r\n[Parallel(n_jobs=-1)]: Done 10469 out of 10500 | elapsed:  2.1min remaining:    0.4s\r\n[Parallel(n_jobs=-1)]: Done 10500 out of 10500 | elapsed:  2.2min finished\r\nRandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\r\n            max_depth=15, max_features=10, max_leaf_nodes=None,\r\n            min_impurity_decrease=0.0, min_impurity_split=None,\r\n            min_samples_leaf=1, min_samples_split=3,\r\n            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\r\n            oob_score=False, random_state=2525, verbose=0,\r\n            warm_start=False)<\/pre>\n<p>gs\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u5b66\u7fd2\uff08\u63a2\u7d22\uff09\u3055\u305b\u308b\u306b\u306f\u3001\u4ed6\u306esklearn\u306e\u30af\u30e9\u30b9\u3068\u540c\u69d8\u306bfit\u30e1\u30bd\u30c3\u30c9\u3092\u4f7f\u3044\u307e\u3059\u3002<\/p>\n<p>\u5b66\u7fd2\u6e08\u307f\u306egs\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e<span style=\"color: #008000;\"><strong>best_estimator_\u5c5e\u6027<\/strong><\/span>\u306b<strong><span style=\"color: #0000ff;\">\u6700\u3082\u6027\u80fd\u304c\u826f\u304b\u3063\u305f\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u304c\u5165\u3063\u3066\u3044\u307e\u3059<\/span><\/strong>\u3002<\/p>\n<p>\u5148\u307b\u3069\u3068\u540c\u69d8\u306b\u3057\u3066\u8a55\u4fa1\u3059\u308b\u3068\u3001<span style=\"color: #ff0000;\"><strong>acc\u306f98.88\uff05<\/strong><\/span>\u3067\u3057\u305f\u3002<\/p>\n<p>\u304b\u306a\u308a\u826f\u304f\u306a\u3063\u3066\u3044\u307e\u3059\u306d\u3002<\/p>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-65521\" src=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits3.png\" alt=\"\" width=\"854\" height=\"847\" srcset=\"https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits3.png 854w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits3-150x150.png 150w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits3-300x298.png 300w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits3-640x635.png 640w, https:\/\/www.sejuku.net\/blog\/wp-content\/uploads\/2018\/07\/digits3-810x803.png 810w\" sizes=\"(max-width: 854px) 100vw, 854px\" \/><\/p>\n<p>\u4e88\u6e2c\u30e9\u30d9\u30eb\u3092\u53ef\u8996\u5316\u3057\u3066\u307f\u308b\u3068\u3001\u5148\u7a0b\u3088\u308a\u3082\u8aa4\u7b54\u304c\u6e1b\u3063\u3066\u3044\u308b\uff08\u8868\u793a\u3057\u3066\u3044\u308b\u7bc4\u56f2\u3067\u306f\u8aa4\u7b54\u30bc\u30ed\uff09\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n<h2>\u307e\u3068\u3081<\/h2>\n<p>\u3053\u306e\u8a18\u4e8b\u3067\u306f<span style=\"color: #0000ff;\"><strong>\u7c21\u5358\u306a\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306e\u8aac\u660e\u3068\u3001\u305d\u306e\u4f7f\u3044\u65b9\u3092\u7d39\u4ecb<\/strong><\/span>\u3057\u307e\u3057\u305f\u3002<\/p>\n<p>\u767a\u5c55\u7684\u306a\u5185\u5bb9\u3068\u3057\u3066\u7d39\u4ecb\u3057\u305f<span style=\"color: #0000ff;\"><strong>\u30d1\u30e9\u30e1\u30bf\u30b5\u30fc\u30c1\u306f\u975e\u5e38\u306b\u6709\u7528\u306a\u65b9\u6cd5<\/strong><\/span>\u3067\u3001\u5b9f\u969b\u306e\u30c7\u30fc\u30bf\u89e3\u6790\u3067\u306f\u5e83\u304f\u4f7f\u308f\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u3082\u3063\u3068\u8a73\u3057\u304f\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u306b\u3064\u3044\u3066\u77e5\u308a\u305f\u3044\u3001\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3092\u81ea\u5206\u3067\u5b9f\u88c5\u3057\u305f\u3044\u3001\u3068\u3044\u3046\u65b9\u306f<a href=\"https:\/\/lp.sejuku.net\/lp1_blog_01\/?cid=ai_btn2_64455\">\u30de\u30f3\u30c4\u30fc\u30de\u30f3\u30ec\u30c3\u30b9\u30f3\u3067\u304a\u5f85\u3061\u3057\u3066\u3044\u307e\u3059<\/a>\u3002<\/p>\n<p>\u5b9f\u7528\u7684\u306a\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u4e00\u3064\u3067\u3042\u308b\u30e9\u30f3\u30c0\u30e0\u30d5\u30a9\u30ec\u30b9\u30c8\u3001\u4f7f\u3044\u3053\u306a\u3057\u3066\u6a5f\u68b0\u5b66\u7fd2\u3092\u8eab\u8fd1\u306a\u3082\u306e\u306b\u3057\u3061\u3083\u3044\u307e\u3057\u3087\u3046\uff01<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deep Learning\u306e\u3088\u3046\u306a\u30d1\u30ef\u30d5\u30eb\u306a\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3082\u3044\u3044\u3067\u3059\u304c\u3001\u3082\u3063\u3068\u624b\u8efd\u306a\u30e2\u30c7\u30eb\u304c\u305f\u304f\u3055\u3093\u3042\u308a\u307e\u3059\u3002 Python\u3068scikit-learn\u3067\u624b\u8efd\u306b\u6a5f\u68b0\u5b66\u7fd2\u3092\u4f53\u9a13\u3057\u3061\u3083\u3044\u307e\u3057\u3087\u3046\uff01 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