{"id":3557,"date":"2023-07-16T20:42:07","date_gmt":"2023-07-16T20:42:07","guid":{"rendered":"https:\/\/statorials.org\/tr\/birden-cok-sutunu-kodlayan-sklearn-etiketi\/"},"modified":"2023-07-16T20:42:07","modified_gmt":"2023-07-16T20:42:07","slug":"birden-cok-sutunu-kodlayan-sklearn-etiketi","status":"publish","type":"post","link":"https:\/\/statorials.org\/tr\/birden-cok-sutunu-kodlayan-sklearn-etiketi\/","title":{"rendered":"Scikit-learn: \u00e7ok s\u00fctunlu etiket kodlamas\u0131n\u0131 kullan\u0131n"},"content":{"rendered":"

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Makine \u00f6\u011frenmesinde etiket kodlama<\/strong> , kategorik bir de\u011fi\u015fkenin<\/a> de\u011ferlerini tam say\u0131 de\u011ferlerine d\u00f6n\u00fc\u015ft\u00fcrme i\u015flemidir.<\/span><\/p>\n

\u00d6rne\u011fin, a\u015fa\u011f\u0131daki ekran g\u00f6r\u00fcnt\u00fcs\u00fc Team<\/strong> ad\u0131 verilen kategorik bir de\u011fi\u015fkenin her benzersiz de\u011ferinin, alfabetik s\u0131raya g\u00f6re bir tamsay\u0131 de\u011ferine nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fini g\u00f6sterir:<\/span> <\/p>\n

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Python’da \u00e7ok s\u00fctunlu etiket kodlamas\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabilirsiniz:<\/span><\/p>\n

 from<\/span> sklearn. preprocessing<\/span> import<\/span> LabelEncoder\n\n#perform label encoding on col1, col2 columns\n<\/span>df[[' col1<\/span> ', ' col2<\/span> ']] = df[[' col1<\/span> ', ' col2<\/span> ']]. apply<\/span> (LabelEncoder(). fit_transform<\/span> )\n<\/strong><\/pre>\n
 A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n
 \u00d6rnek: Python’da etiketleri kodlamak<\/strong><\/span><\/h2>\n
 \u00c7e\u015fitli basketbol oyuncular\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki pandalar DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n
 import<\/span> pandas as<\/span> pd\n\n#createDataFrame<\/span>\ndf = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'C', 'C', 'D'],\n                   ' position<\/span> ': ['G', 'F', 'G', 'F', 'F', 'G', 'G', 'F'],\n                   ' all_star<\/span> ': ['Y', 'N', 'Y', 'Y', 'Y', 'N', 'Y', 'N'],\n                   ' points<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team position all_star points\n0 AGY 11\n1 AFN 8\n2 BGY 10\n3 BFY 6\n4 BFY 6\n5 CGN 5\n6 CGY 9\n7 DFN 12\n<\/strong><\/span><\/pre>\n
 Team<\/strong> , pozisyon<\/strong> ve all_star<\/strong> s\u00fctunlar\u0131ndaki her kategorik de\u011feri tam say\u0131 de\u011ferlerine d\u00f6n\u00fc\u015ft\u00fcrmek amac\u0131yla etiket kodlamas\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n
 from<\/span> sklearn. preprocessing<\/span> import<\/span> LabelEncoder\n\n#perform label encoding across team, position, and all_star columns\ndf[[' team<\/span> ', ' position<\/span> ', ' all_star<\/span> ']] = df[[' team<\/span> ', ' position<\/span> ', ' all_star<\/span> ']]. apply<\/span> (LabelEncoder(). fit_transform<\/span> )\n<\/span>\n#view udpated DataFrame\nprint<\/span> (df)\n\n   team position all_star points\n0 0 1 1 11\n1 0 0 0 8\n2 1 1 1 10\n3 1 0 1 6\n4 1 0 1 6\n5 2 1 0 5\n6 2 1 1 9\n7 3 0 0 12<\/span><\/span><\/strong><\/pre>\n
 Sonu\u00e7ta tak\u0131m<\/strong> , pozisyon<\/strong> ve all_star<\/strong> s\u00fctunlar\u0131ndaki her bir de\u011ferin tam say\u0131 de\u011ferlerine d\u00f6n\u00fc\u015ft\u00fcr\u00fcld\u00fc\u011f\u00fcn\u00fc g\u00f6rebiliriz.<\/span><\/p>\n
 \u00d6rne\u011fin, ekip<\/strong> s\u00fctununda \u015funlar\u0131 g\u00f6rebiliriz:<\/span><\/p>\n