{"id":7088,"date":"2025-08-28T10:58:53","date_gmt":"2025-08-28T01:58:53","guid":{"rendered":"https:\/\/blog.since2020.jp\/?p=7088"},"modified":"2025-08-28T10:58:53","modified_gmt":"2025-08-28T01:58:53","slug":"pca_basket","status":"publish","type":"post","link":"https:\/\/since2020.jp\/media\/pca_basket\/","title":{"rendered":"\u4e3b\u6210\u5206\u5206\u6790\u306b\u3088\u308b\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u306e\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0"},"content":{"rendered":"\n

\u30de\u30fc\u30b1\u30c3\u30c8\u30d0\u30b9\u30b1\u30c3\u30c8\u5206\u6790\u3092\u5b9f\u65bd\u3059\u308b\u3068\u3001\u6570\u767e\u304b\u3089\u6570\u5343\u306e\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u304c\u751f\u6210\u3055\u308c\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u300c\u3069\u306e\u30eb\u30fc\u30eb\u304c\u672c\u5f53\u306b\u4fa1\u5024\u304c\u3042\u308b\u306e\u304b\uff1f\u300d\u3068\u3044\u3046\u6839\u672c\u7684\u306a\u554f\u984c\u306b\u76f4\u9762\u3057\u305f\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u304b\uff1f\u672c\u8a18\u4e8b\u3067\u306f\u3001\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u3092\u6d3b\u7528\u3057\u3066\u3001\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u3092\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0\u3059\u308b\u65b9\u6cd5\u3092\u8003\u3048\u3066\u307f\u307e\u3057\u305f\u3002<\/p>\n\n\n

\u57fa\u672c3\u6307\u6a19\u306e\u304a\u3055\u3089\u3044<\/h2>\n

\u307e\u305a\u3001\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u5206\u6790\u306e\u57fa\u672c\u3068\u306a\u308b3\u3064\u306e\u6307\u6a19\u306b\u3064\u3044\u3066\u78ba\u8a8d\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046\u3002<\/p>\r\n

 <\/p>\r\n

\u652f\u6301\u5ea6\uff08Support\uff09\u306f\u3001\u5168\u53d6\u5f15\u306b\u5360\u3081\u308b\u8a72\u5f53\u30eb\u30fc\u30eb\u306e\u767a\u751f\u983b\u5ea6\u3092\u8868\u3057\u307e\u3059\u3002\u6570\u5b66\u7684\u306b\u306f\u4ee5\u4e0b\u3067\u5b9a\u7fa9\u3055\u308c\u307e\u3059\uff1a<\/p>\r\n

\r\n
Support(A \u2192 B) = P(A \u2229 B) = |A \u2229 B| \/ |D|<\/code><\/pre>\r\n<\/div>\r\n
\u3053\u3053\u3067\u3001|A \u2229 B|\u306fA\u3068B\u304c\u540c\u6642\u306b\u767a\u751f\u3057\u305f\u53d6\u5f15\u6570\u3001|D|\u306f\u5168\u53d6\u5f15\u6570\u3092\u8868\u3057\u307e\u3059\u3002\u4f8b\u3048\u3070\u652f\u6301\u5ea60.1\u306a\u3089\u5168\u53d6\u5f15\u306e10%\u3067\u305d\u306e\u30eb\u30fc\u30eb\u304c\u767a\u751f\u3057\u3066\u3044\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u3001\u58f2\u4e0a\u30dc\u30ea\u30e5\u30fc\u30e0\u3084\u30d3\u30b8\u30cd\u30b9\u30a4\u30f3\u30d1\u30af\u30c8\u306e\u5927\u304d\u3055\u3092\u5224\u65ad\u3059\u308b\u969b\u306b\u91cd\u8981\u3067\u3059\u3002<\/p>\r\n
 <\/p>\r\n
\u4fe1\u983c\u5ea6\uff08Confidence\uff09\u306f\u3001\u6761\u4ef6\u3068\u306a\u308b\u5546\u54c1A\u304c\u8cfc\u5165\u3055\u308c\u305f\u5834\u5408\u306b\u3001\u7d50\u679c\u3068\u306a\u308b\u5546\u54c1B\u3082\u8cfc\u5165\u3055\u308c\u308b\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u3092\u793a\u3057\u307e\u3059\uff1a<\/p>\r\n
\r\n
Confidence(A \u2192 B) = P(B|A) = P(A \u2229 B) \/ P(A) = |A \u2229 B| \/ |A|<\/code><\/pre>\r\n<\/div>\r\n
\u4fe1\u983c\u5ea60.8\u306a\u3089\u300cA\u3092\u8cb7\u3063\u305f\u4eba\u306e80%\u304cB\u3082\u8cfc\u5165\u3059\u308b\u300d\u3053\u3068\u3092\u8868\u3057\u3001\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30b7\u30b9\u30c6\u30e0\u306e\u7cbe\u5ea6\u306b\u76f4\u7d50\u3059\u308b\u6307\u6a19\u3067\u3059\u3002<\/p>\r\n
 <\/p>\r\n
\u30ea\u30d5\u30c8\u5024\uff08Lift\uff09\u306f\u3001\u5076\u7136\u4ee5\u4e0a\u306e\u95a2\u9023\u6027\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u3092\u6e2c\u308b\u6307\u6a19\u3067\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u5b9a\u7fa9\u3055\u308c\u307e\u3059\uff1a<\/p>\r\n
\r\n
Lift(A \u2192 B) = P(B|A) \/ P(B) = Confidence(A \u2192 B) \/ Support(B)<\/code><\/pre>\r\n<\/div>\r\n
\u30ea\u30d5\u30c8\u5024\u304c1.0\u306a\u3089\u5076\u7136\u3068\u540c\u3058\u30012.0\u306a\u3089\u5076\u7136\u306e2\u500d\u306e\u78ba\u7387\u3067B\u304c\u8cfc\u5165\u3055\u308c\u308b\u3053\u3068\u3092\u610f\u5473\u3057\u3001\u5546\u54c1\u9593\u306e\u771f\u306e\u95a2\u9023\u6027\u3092\u767a\u898b\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/p>\n\n
\u306a\u305c\u5f93\u6765\u306e\u8a55\u4fa1\u65b9\u6cd5\u3067\u306f\u9650\u754c\u304c\u3042\u308b\u306e\u304b\uff1f<\/h2>\n
\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u5206\u6790\u306e\u73fe\u5834\u3067\u3088\u304f\u906d\u9047\u3059\u308b\u306e\u304c\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u77db\u76fe\u3067\u3059\uff1a<\/p>\r\n
\u30eb\u30fc\u30ebA: \u30d1\u30f3 \u2192 \u30d0\u30bf\u30fc<\/p>\r\n
– \u652f\u6301\u5ea6: 0.15\uff0815%\u306e\u53d6\u5f15\u3067\u767a\u751f\uff09
\r\n– \u4fe1\u983c\u5ea6: 0.7\uff0870%\u306e\u78ba\u7387\uff09
\r\n– \u30ea\u30d5\u30c8\u5024: 1.8<\/p>\r\n
\u30eb\u30fc\u30ebB: \u9ad8\u7d1a\u30ef\u30a4\u30f3 \u2192 \u30c8\u30ea\u30e5\u30d5<\/p>\r\n
– \u652f\u6301\u5ea6: 0.005\uff080.5%\u306e\u53d6\u5f15\u3067\u767a\u751f\uff09
\r\n– \u4fe1\u983c\u5ea6: 0.95\uff0895%\u306e\u78ba\u7387\uff09
\r\n– \u30ea\u30d5\u30c8\u5024: 8.0<\/p>\r\n
\u3069\u3061\u3089\u304c\u300c\u512a\u79c0\u300d\u306a\u30eb\u30fc\u30eb\u3067\u3057\u3087\u3046\u304b\uff1f<\/p>\r\n
\u30eb\u30fc\u30ebA\u306f\u983b\u5ea6\u304c\u9ad8\u304f\u5b89\u5b9a\u3057\u3066\u3044\u307e\u3059\u304c\u3001\u30ea\u30d5\u30c8\u5024\u304c\u5c0f\u3055\u3044\u3001\u3059\u306a\u308f\u3061<\/span>\u9a5a\u304d\u306f\u5c11\u306a\u3044\u3068\u8a00\u3048\u307e\u3059\u3002\u4e00\u65b9\u3001\u30eb\u30fc\u30ebB\u306f\u7a00\u3060\u304c\u5f37\u3044\u95a2\u9023\u6027\u3092\u793a\u3057\u3066\u3044\u307e\u3059\u3002\u5f93\u6765\u306e\u8a55\u4fa1\u3067\u306f\u3001\u3053\u306e\u5224\u65ad\u3092\u5206\u6790\u8005\u306e\u4e3b\u89b3\u306b\u59d4\u306d\u308b\u3053\u3068\u304c\u591a\u304f\u3001\u5ba2\u89b3\u6027\u306b\u6b20\u3051\u308b\u3068\u3044\u3046\u554f\u984c\u304c\u3042\u308a\u307e\u3057\u305f\u3002<\/p>\r\n
\u307e\u305f\u3001\u591a\u304f\u306e\u5b9f\u52d9\u62c5\u5f53\u8005\u304c\u300c\u652f\u6301\u5ea630%\u3001\u4fe1\u983c\u5ea640%\u3001\u30ea\u30d5\u30c8\u502430%\u300d\u306e\u3088\u3046\u306a\u4e3b\u89b3\u7684\u306a\u91cd\u307f\u4ed8\u3051\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u304c\u3001\u3053\u306e\u6839\u62e0\u306f\u4f55\u3067\u3057\u3087\u3046\u304b\uff1f\u696d\u754c\u3084\u76ee\u7684\u306b\u3088\u3063\u3066\u6700\u9069\u306a\u91cd\u307f\u306f\u5909\u308f\u308b\u306f\u305a\u3067\u3059\u3002<\/p>\n\n
PCA\u306b\u3088\u308b\u5ba2\u89b3\u7684\u91cd\u307f\u6c7a\u5b9a\uff1a\u30c7\u30fc\u30bf\u304c\u7b54\u3048\u3092\u6559\u3048\u3066\u304f\u308c\u308b<\/h2>\n
\u4e3b\u6210\u5206\u5206\u6790\uff08PCA\uff09\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u81ea\u8eab\u304b\u3089\u6700\u9069\u306a\u91cd\u307f\u4ed8\u3051\u3092\u5c0e\u304d\u51fa\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u3053\u306e\u624b\u6cd5\u306e\u6838\u3068\u306a\u308b\u8003\u3048\u65b9\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff1a<\/p>\r\n
\r\n
\u300c\u30c7\u30fc\u30bf\u306e\u5206\u6563\u3092\u6700\u5927\u5316\u3059\u308b\u65b9\u5411\u304c\u3001\u6700\u3082\u60c5\u5831\u91cf\u306e\u591a\u3044\u65b9\u5411\u3067\u3042\u308b\u300d<\/p>\r\n<\/blockquote>\r\n
\u3053\u306e\u8003\u3048\u65b9\u3092\u7528\u3044\u3066\u3001\u91cd\u307f\u306e\u7b97\u51fa\u3084\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u306e\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0\u3092\u884c\u3044\u307e\u3059\u3002<\/p>\r\n
<\/p>\n\n
PCA\u306b\u3088\u308b\u91cd\u307f\u7b97\u51fa<\/h2>\n
\u4ee5\u4e0b\u306f\u3001\u5b9f\u969b\u306bPCA\u3092\u7528\u3044\u3066\u91cd\u307f\u3092\u7b97\u51fa\u3059\u308bPython\u306e\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u3067\u3059\uff1a<\/p>\r\n
import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.decomposition import PCA\r\nfrom sklearn.preprocessing import StandardScaler\r\n\r\ndef calculate_pca_weights(data):\r\n    \"\"\"PCA\u306b\u3088\u308b\u5ba2\u89b3\u7684\u91cd\u307f\u8a08\u7b97\"\"\"\r\n    features = ['support', 'confidence', 'lift']\r\n    X = data[features].values\r\n    \r\n    # \u6a19\u6e96\u5316\r\n    scaler = StandardScaler()\r\n    X_scaled = scaler.fit_transform(X)\r\n    \r\n    # PCA\u5b9f\u884c\r\n    pca = PCA()\r\n    pca.fit(X_scaled)\r\n    \r\n    # \u7b2c1\u4e3b\u6210\u5206\u306e\u8ca0\u8377\u91cf\u304b\u3089\u91cd\u307f\u8a08\u7b97\r\n    pc1_loadings = np.abs(pca.components_[0])\r\n    weights = pc1_loadings \/ pc1_loadings.sum()\r\n    \r\n    return {\r\n        'w_support': weights[0],\r\n        'w_confidence': weights[1],\r\n        'w_lift': weights[2],\r\n        'explained_variance': pca.explained_variance_ratio_[0]\r\n    }\r\n\r\n# \u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\uff0812\u500b\u306e\u30eb\u30fc\u30eb\uff09\r\nsample_data = pd.DataFrame({\r\n    'itemset_A': ['\u304a\u306b\u304e\u308a', '\u30b3\u30fc\u30d2\u30fc', '\u30d3\u30fc\u30eb', '\u30a2\u30a4\u30b9', '\u304a\u5f01\u5f53', '\u30bf\u30d0\u30b3', \r\n                 '\u30d1\u30f3', '\u304a\u8336', '\u304a\u83d3\u5b50', '\u30c9\u30ea\u30f3\u30af', '\u30b5\u30e9\u30c0', '\u30c7\u30b6\u30fc\u30c8'],\r\n    'itemset_B': ['\u304a\u8336', '\u30b5\u30f3\u30c9\u30a4\u30c3\u30c1', '\u304a\u3064\u307e\u307f', '\u30c1\u30e7\u30b3', '\u30b5\u30e9\u30c0', '\u30e9\u30a4\u30bf\u30fc',\r\n                 '\u30b8\u30e3\u30e0', '\u304a\u306b\u304e\u308a', '\u30b8\u30e5\u30fc\u30b9', '\u30a2\u30a4\u30b9', '\u30c9\u30ec\u30c3\u30b7\u30f3\u30b0', '\u30b3\u30fc\u30d2\u30fc'],\r\n    'support': [0.25, 0.15, 0.08, 0.30, 0.12, 0.05, \r\n               0.18, 0.22, 0.35, 0.20, 0.14, 0.28],\r\n    'confidence': [0.85, 0.90, 0.75, 0.60, 0.88, 0.95,\r\n                  0.72, 0.80, 0.65, 0.78, 0.83, 0.70],\r\n    'lift': [3.2, 4.1, 2.8, 1.9, 3.5, 5.2,\r\n            2.4, 3.0, 1.8, 2.6, 3.8, 2.2]\r\n})\r\n\r\n# \u91cd\u307f\u8a08\u7b97\r\nweights = calculate_pca_weights(sample_data)<\/code><\/pre>\r\n
\u5b9f\u884c\u7d50\u679c\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\u3002<\/p>\r\n
\r\n
PCA\u91cd\u307f\u7d50\u679c:\r\n\u652f\u6301\u5ea6\u91cd\u307f: 0.317\r\n\u4fe1\u983c\u5ea6\u91cd\u307f: 0.339\r\n\u30ea\u30d5\u30c8\u91cd\u307f: 0.344\r\n\u7b2c1\u4e3b\u6210\u5206\u8aac\u660e\u529b: 0.880<\/code><\/pre>\r\n<\/div>\r\n
\u3053\u3053\u3067\u3001PCA\u3067\u7b97\u51fa\u3057\u305f\u91cd\u307f\u306e\u4fe1\u983c\u6027\u306f\u3001\u7b2c1\u4e3b\u6210\u5206\u306e\u5bc4\u4e0e\u7387\u3067\u5224\u65ad\u3067\u304d\u307e\u3059\u3002\u5bc4\u4e0e\u7387\u306b\u3088\u308b\u4fe1\u983c\u6027\u306e\u5224\u65ad\u306e\u76ee\u5b89\u306f\u4ee5\u4e0b\u306e\u901a\u308a\u3067\u3059\uff1a<\/p>\r\n
    \r\n\t
  • \u30fb\u5bc4\u4e0e\u7387 \u2265 0.6<\/strong>: \u975e\u5e38\u306b\u4fe1\u983c\u6027\u304c\u9ad8\u3044<\/li>\r\n\t
  • \u30fb\u5bc4\u4e0e\u7387 0.4-0.6<\/strong>: \u4fe1\u983c\u6027\u304c\u9ad8\u3044<\/li>\r\n\t
  • \u30fb\u5bc4\u4e0e\u7387 0.3-0.4<\/strong>: \u4e2d\u7a0b\u5ea6\u306e\u4fe1\u983c\u6027<\/li>\r\n\t
  • \u30fb\u5bc4\u4e0e\u7387 < 0.3<\/strong>: \u4ed6\u624b\u6cd5\u306e\u691c\u8a0e\u63a8\u5968<\/li>\r\n<\/ul>\r\n
    \u4eca\u56de\u306f\u3001\u7b2c1\u4e3b\u6210\u5206\u306e\u8aac\u660e\u529b: 0.880<\/code>\u3068\u306a\u3063\u3066\u3044\u308b\u306e\u3067\u3001\u91cd\u307f\u306e\u4fe1\u983c\u6027\u304c\u975e\u5e38\u306b\u9ad8\u3044\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\r\n
     <\/p>\r\n
     <\/p>\n\n
    PCA\u91cd\u307f\u3092\u4f7f\u3063\u305f\u7dcf\u5408\u30b9\u30b3\u30a2\u7b97\u51fa<\/h2>\n
    PCA\u3092\u7528\u3044\u3066\u91cd\u307f\u3092\u7b97\u51fa\u3092\u884c\u3063\u305f\u306e\u3067\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u3001\u7dcf\u5408\u30b9\u30b3\u30a2\u306e\u7b97\u51fa\u3092\u3057\u307e\u3057\u305f\u3002<\/p>\r\n
     <\/p>\r\n
    def calculate_comprehensive_score(data, pca_weights):\r\n    \"\"\"PCA\u91cd\u307f\u306b\u3088\u308b\u7dcf\u5408\u30b9\u30b3\u30a2\u7b97\u51fa\"\"\"\r\n    # \u30ea\u30d5\u30c8\u5024\u306e\u6b63\u898f\u5316\uff081\u3092\u5f15\u3044\u30660\u57fa\u6e96\u306b\u3059\u308b\uff09\r\n    lift_normalized = data['lift'] - 1\r\n    \r\n    # \u7dcf\u5408\u30b9\u30b3\u30a2\u8a08\u7b97\r\n    scores = (\r\n        pca_weights['w_support'] * data['support'] +\r\n        pca_weights['w_confidence'] * data['confidence'] +\r\n        pca_weights['w_lift'] * lift_normalized\r\n    )\r\n    \r\n    return scores\r\n\r\n# \u524d\u306e\u30b3\u30fc\u30c9\u3067\u7b97\u51fa\u3057\u305f\u91cd\u307f\u3092\u4f7f\u7528\r\nsample_data['pca_score'] = calculate_comprehensive_score(sample_data, weights)\r\n\r\n# \u4e0a\u4f4d5\u30eb\u30fc\u30eb\u306e\u62bd\u51fa\r\ntop_rules = sample_data.nlargest(5, 'pca_score')\r\nprint(f\"\\nPCA\u30b9\u30b3\u30a2\u4e0a\u4f4d5\u30eb\u30fc\u30eb:\")\r\nprint(top_rules[['itemset_A', 'itemset_B', 'pca_score']])\r\n<\/code><\/pre>\r\n
    \u3010\u5b9f\u884c\u7d50\u679c\u3011<\/p>\r\n
    \r\n
    PCA\u30b9\u30b3\u30a2\u4e0a\u4f4d5\u30eb\u30fc\u30eb:\r\nitemset_A itemset_B pca_score\r\n5 \u30bf\u30d0\u30b3 \u30e9\u30a4\u30bf\u30fc 1.784154\r\n1 \u30b3\u30fc\u30d2\u30fc \u30b5\u30f3\u30c9\u30a4\u30c3\u30c1 1.420042\r\n10 \u30b5\u30e9\u30c0 \u30c9\u30ec\u30c3\u30b7\u30f3\u30b0 1.289840\r\n4 \u304a\u5f01\u5f53 \u30b5\u30e9\u30c0 1.197103\r\n0 \u304a\u306b\u304e\u308a \u304a\u8336 1.124817<\/code><\/pre>\r\n<\/div>\r\n
    \u3053\u308c\u306b\u3088\u3063\u3066\u3001\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u3092\u30b9\u30b3\u30a2\u30ea\u30f3\u30b0\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3057\u305f\uff01<\/p>\r\n
    \u300c\u30bf\u30d0\u30b3\u3092\u8cb7\u3046\u4eba\u306f\u30e9\u30a4\u30bf\u30fc\u3082\u8cb7\u3046\u300d\u3068\u3044\u3046\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u304c\u3001\u58f2\u4e0a\u30dc\u30ea\u30e5\u30fc\u30e0(\u652f\u6301\u5ea6)\u30fb\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u7cbe\u5ea6(\u4fe1\u983c\u5ea6)\u30fb\u610f\u5916\u306a\u5546\u54c1\u306e\u7d44\u307f\u5408\u308f\u305b(\u30ea\u30d5\u30c8\u5024)\u3092\u7dcf\u5408\u7684\u306b\u8003\u616e\u3057\u305f\u30b9\u30b3\u30a2\u304c\u4e00\u756a\u9ad8\u3044\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059\u3002<\/p>\r\n
    \u307e\u305f\u30012\u756a\u76ee\u306e\u300c\u30b3\u30fc\u30d2\u30fc\u3092\u8cb7\u3046\u4eba\u306f\u30b5\u30f3\u30c9\u30a4\u30c3\u30c1\u3082\u8cb7\u3046\u300d\u3068\u3044\u3046\u30eb\u30fc\u30eb\u306e\u30b9\u30b3\u30a2\u3082\u9ad8\u3044\u3068\u3044\u3046\u306e\u3082\u9762\u767d\u3044\u3067\u3059\u3002<\/p>\n\n
    \u307e\u3068\u3081<\/h2>\n
    PCA\u3092\u6d3b\u7528\u3057\u305f\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u8a55\u4fa1\u306b\u3088\u308a\u3001\u5f93\u6765\u306e\u4e3b\u89b3\u7684\u306a\u91cd\u307f\u4ed8\u3051\u304b\u3089\u8131\u5374\u3057\u3001\u30c7\u30fc\u30bf\u81ea\u8eab\u304c\u6559\u3048\u308b\u5ba2\u89b3\u7684\u306a\u57fa\u6e96\u3067\u30eb\u30fc\u30eb\u306e\u512a\u52a3\u3092\u5224\u5b9a\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002\u7b2c1\u4e3b\u6210\u5206\u306e\u5bc4\u4e0e\u7387\u306b\u3088\u3063\u3066\u91cd\u307f\u306e\u4fe1\u983c\u6027\u3092\u5b9a\u91cf\u7684\u306b\u8a55\u4fa1\u3067\u304d\u3001\u7d99\u7d9a\u7684\u306b\u30c7\u30fc\u30bf\u3092\u84c4\u7a4d\u3059\u308b\u3053\u3068\u3067\u8a55\u4fa1\u7cbe\u5ea6\u3092\u5411\u4e0a\u3055\u305b\u3089\u308c\u308b\u305f\u3081\u3001\u3088\u308a\u52b9\u679c\u7684\u3067\u8aac\u660e\u53ef\u80fd\u306a\u30d3\u30b8\u30cd\u30b9\u610f\u601d\u6c7a\u5b9a\u3092\u5b9f\u73fe\u3067\u304d\u308b\u3067\u3057\u3087\u3046\u3002<\/p>","protected":false},"excerpt":{"rendered":"
    \u30de\u30fc\u30b1\u30c3\u30c8\u30d0\u30b9\u30b1\u30c3\u30c8\u5206\u6790\u3092\u5b9f\u65bd\u3059\u308b\u3068\u3001\u6570\u767e\u304b\u3089\u6570\u5343\u306e\u30a2\u30bd\u30b7\u30a8\u30fc\u30b7\u30e7\u30f3\u30eb\u30fc\u30eb\u304c\u751f\u6210\u3055\u308c\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u300c\u3069\u306e\u30eb\u30fc\u30eb\u304c\u672c\u5f53\u306b\u4fa1\u5024\u304c\u3042\u308b\u306e\u304b\uff1f\u300d\u3068\u3044\u3046\u6839\u672c\u7684\u306a\u554f\u984c\u306b\u76f4\u9762\u3057\u305f\u3053\u3068\u306f\u3042\u308a\u307e\u305b\u3093\u304b\uff1f\u672c\u8a18\u4e8b\u3067\u306f\u3001\u4e3b\u6210\u5206\u5206\u6790\uff08 […]<\/p>\n","protected":false},"author":89,"featured_media":3651,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","swell_btn_cv_data":"","footnotes":"","_wp_rev_ctl_limit":""},"categories":[1246],"tags":[331,39,33],"class_list":["post-7088","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-infrastructure","tag-python","tag-39","tag-33"],"_links":{"self":[{"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/posts\/7088","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\/89"}],"replies":[{"embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/comments?post=7088"}],"version-history":[{"count":0,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/posts\/7088\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/media\/3651"}],"wp:attachment":[{"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/media?parent=7088"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/categories?post=7088"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/since2020.jp\/media\/wp-json\/wp\/v2\/tags?post=7088"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}