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\uc0dd\uc131\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc \uba87 \ub144\uac04 \ub9ce\uc740 \uad00\uc2ec\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub525 \ub7ec\ub2dd \uae30\uc220\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ubc1c\uc804\uc740 \ub9ce\uc740 \uc5f0\uad6c\uc790\uc640 \uac1c\ubc1c\uc790\ub4e4\uc5d0\uac8c \uc0c8\ub85c\uc6b4 \uac00\ub2a5\uc131\uc744 \uc5f4\uc5b4\uc8fc\uc5c8\uc2b5\ub2c8\ub2e4.\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25197,"url":"https:\/\/atmokpo.com\/w\/25197\/","url_meta":{"origin":25235,"position":3},"title":"03-02 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud1b5\uacc4\uc801 \uc5b8\uc5b4 \ubaa8\ub378(Statistical Language Model, SLM)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: 2023\ub144 10\uc6d4 05\uc77c \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud558\ub294 \uae30\uc220 \ubd84\uc57c\ub85c, \ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5(AI)\uacfc \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc5d0 \ud798\uc785\uc5b4 \ud06c\uac8c \ubc1c\uc804\ud558\uc600\ub2e4. \ud2b9\ud788, \ud1b5\uacc4\uc801 \uc5b8\uc5b4 \ubaa8\ub378(Statistical Language Model, SLM)\uc740 \uc774\ub7ec\ud55c \ubc1c\uc804\uc758 \ud575\uc2ec\uc801\uc778 \uc694\uc18c\ub85c \uc790\ub9ac \uc7a1\uace0 \uc788\ub2e4. \ubcf8 \uae00\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25269,"url":"https:\/\/atmokpo.com\/w\/25269\/","url_meta":{"origin":25235,"position":4},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ud53c\ub4dc \ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd \uc5b8\uc5b4 \ubaa8\ub378 (Neural Network Language Model, NNLM)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc790: \uc870\uad11\ud615 \uc791\uc131\uc77c: 2024\ub144 11\uc6d4 26\uc77c 1. \uc11c\ub860 \ucd5c\uadfc \uba87 \ub144\uac04 \uc778\uacf5\uc9c0\ub2a5 \ubd84\uc57c\uc5d0\uc11c \ub525 \ub7ec\ub2dd(deep learning)\uc758 \ubc1c\uc804\uc740 \ub9e4\uc6b0 \ub208\ubd80\uc2e0 \ubcc0\ud654\uc640 \ud601\uc2e0\uc744 \uac00\uc838\uc654\uc2b5\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd\uc740 \ud2b9\ud788 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP) \ubd84\uc57c\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc73c\uba70, \uadf8\uc5d0 \ub530\ub77c \ub2e4\uc591\ud55c \ubaa8\ub378\ub4e4\uc774 \uac1c\ubc1c\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \ud53c\ub4dc \ud3ec\uc6cc\ub4dc \uc2e0\uacbd\ub9dd \uc5b8\uc5b4 \ubaa8\ub378(Neural Network Language\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25421,"url":"https:\/\/atmokpo.com\/w\/25421\/","url_meta":{"origin":25235,"position":5},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, BERT(Bidirectional Encoder Representations from Transformers)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: [\ub0a0\uc9dc] \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP, Natural Language Processing)\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud558\ub294 \ubd84\uc57c\ub85c, \ucd5c\uadfc \uba87 \ub144 \uac04 \uae09\uaca9\ud55c \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc2ec\uc5d0\ub294 \ub525 \ub7ec\ub2dd\uc774 \uc788\uc73c\uba70, \uc774\ub294 \ub9ce\uc740 \ubb38\uc81c\ub4e4\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ud574\uacb0\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4. \uadf8 \uc911\uc5d0\uc11c\ub3c4 BERT(Bidirectional Encoder Representations from Transformers)\ub294 \ud2b9\ud788 \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8\u2026","rel":"","context":""\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac"\uc5d0\uc11c","block_context":{"text":"\ub525\ub7ec\ub2dd 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{"id":25235,"date":"2024-10-26T09:53:14","date_gmt":"2024-10-26T09:53:14","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25235"},"modified":"2024-11-26T08:02:27","modified_gmt":"2024-11-26T08:02:27","slug":"%eb%94%a5-%eb%9f%ac%eb%8b%9d%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-%eb%a1%9c%ec%a7%80%ec%8a%a4%ed%8b%b1-%ed%9a%8c%ea%b7%80-%ec%8b%a4%ec%8a%b5","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25235\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc2e4\uc2b5"},"content":{"rendered":"

<\/p>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \ucef4\ud4e8\ud130\uc640 \uc778\uac04 \uc5b8\uc5b4 \uac04\uc758 \uc0c1\ud638\uc791\uc6a9\uc744 \ud3ec\ud568\ud558\ub294 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 NLP \ubd84\uc57c\ub3c4 \ub9ce\uc740 \ubcc0\ud654\ub97c \uacaa\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ube48\ubc88\ud788 \uc0ac\uc6a9\ub418\ub294 \uae30\ucd08\uc801\uc778 \uae30\ubc95 \uc911 \ud558\ub098\ub85c, \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc774\uc6a9\ud55c \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n

1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub780?<\/h2>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub780 \uc790\uc5f0\uc5b4\ub97c \uc774\ud574\ud558\uace0 \uc0dd\uc131\ud558\ub294 \ucef4\ud4e8\ud130 \uc2dc\uc2a4\ud15c\uc758 \uac1c\ubc1c\uc744 \ud3ec\ud568\ud558\ub294 \ubd84\uc57c\uc785\ub2c8\ub2e4. \uc774 \uae30\uc220\uc740 \uac80\uc0c9 \uc5d4\uc9c4, \ucc57\ubd07, \ud14d\uc2a4\ud2b8 \uc694\uc57d, \uac10\uc815 \ubd84\uc11d \ub4f1 \ub2e4\uc591\ud55c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc5d0\uc11c \ud65c\uc6a9\ub429\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uc8fc\uc694 \uacfc\uc81c \uc911 \uc77c\ubd80\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n

\uc5b8\uc5b4 \ubaa8\ub378\ub9c1:<\/strong> \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8\uc758 \ub2e4\uc74c \ub2e8\uc5b4\ub97c \uc608\uce21\ud558\ub294 \ubaa8\ub378\uc744 \ud559\uc2b5\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/li>\n
\ud14d\uc2a4\ud2b8 \ubd84\ub958:<\/strong> \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8\ub97c \ub77c\ubca8 \ub610\ub294 \uce74\ud14c\uace0\ub9ac\ub85c \ubd84\ub958\ud558\ub294 \uc791\uc5c5\uc785\ub2c8\ub2e4.<\/li>\n
\uc790\uc5f0\uc5b4 \uc0dd\uc131:<\/strong> \uc8fc\uc5b4\uc9c4 \uc785\ub825\uc5d0 \ub530\ub77c \uc0c8\ub85c\uc6b4 \uc790\uc5f0\uc5b4 \ubb38\uc7a5\uc744 \uc0dd\uc131\ud558\ub294 \uc791\uc5c5\uc785\ub2c8\ub2e4.<\/li>\n

\uac10\uc815 \ubd84\uc11d:<\/strong> \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8\uc758 \uac10\uc815\uc744 \uc2dd\ubcc4\ud558\ub294 \uc791\uc5c5\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n
2. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\ub780?<\/h2>\n
\ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \ud1b5\uacc4\uc801 \ubaa8\ub378\ub9c1 \uae30\ubc95\uc73c\ub85c \uc8fc\ub85c \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc120\ud615 \ud68c\uadc0\uc640\ub294 \ub2ec\ub9ac, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 Sigmoid \ud568\uc218(\ub85c\uc9c0\uc2a4\ud2f1 \ud568\uc218)\ub97c \uc0ac\uc6a9\ud558\uc5ec \ucd9c\ub825\uac12\uc744 0\uacfc 1 \uc0ac\uc774\uc758 \ud655\ub960\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4. \uc774 \ub54c\ubb38\uc5d0 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc8fc\uc5b4\uc9c4 \uc785\ub825 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \ud2b9\uc815 \ud074\ub798\uc2a4\uc5d0 \uc18d\ud560 \ud655\ub960\uc744 \uc608\uce21\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
\n P(Y=1|X) = 1 \/ (1 + e^(-z))\n z = \u03b20 + \u03b21X1 + \u03b22X2 + ... + \u03b2nXn\n <\/code><\/pre>\n3. \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\uc758 \ud65c\uc6a9<\/h2>\n\uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc8fc\ub85c \ud14d\uc2a4\ud2b8 \ubd84\ub958 \uc791\uc5c5\uc5d0 \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2a4\ud338 \uc774\uba54\uc77c \ubd84\ub958, \ub274\uc2a4 \uae30\uc0ac \uc8fc\uc81c \ubd84\ub958 \ub4f1 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ud65c\uc6a9\ub429\ub2c8\ub2e4. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \uc8fc\uc5b4\uc9c4 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0\uc11c \ud2b9\uc9d5(feature)\uc744 \ucd94\ucd9c\ud558\uace0, \uc774\ub97c \ud1b5\ud574 \ud574\ub2f9 \ud14d\uc2a4\ud2b8\uac00 \ud2b9\uc815 \ud074\ub798\uc2a4\uc5d0 \uc18d\ud560 \ud655\ub960\uc744 \uc608\uce21\ud569\ub2c8\ub2e4.<\/p>\n 4. \uc2e4\uc2b5 \ud658\uacbd \uc124\uc815<\/h2>\n\ubcf8 \uc2e4\uc2b5\uc5d0\uc11c\ub294 Python\uacfc \uba87 \uac00\uc9c0 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \ubaa9\ub85d\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n \nnumpy<\/li>\n pandas<\/li>\n scikit-learn<\/li>\n matplotlib<\/li>\n seaborn<\/li>\n nltk<\/li>\n<\/ul>\n\ub2e4\uc74c \uba85\ub839\uc5b4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud558\uc138\uc694.<\/p>\n pip install numpy pandas scikit-learn matplotlib seaborn nltk<\/code><\/pre>\n5. \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \uc804\ucc98\ub9ac<\/h2>\n\uc774\ubc88 \uc2e4\uc2b5\uc5d0\uc11c\ub294 \uc774\uba54\uc77c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2a4\ud338 \uc774\uba54\uc77c \ubd84\ub958\uae30\ub97c \ub9cc\ub4dc\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud55c \ud6c4, \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uce69\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc778 \uc804\ucc98\ub9ac \ub2e8\uacc4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n \n\uc18c\ubb38\uc790 \ubcc0\ud658: \ubaa8\ub4e0 \ub2e8\uc5b4\ub97c \uc18c\ubb38\uc790\ub85c \ubcc0\ud658\ud558\uc5ec \uc77c\uad00\uc131\uc744 \uc720\uc9c0\ud569\ub2c8\ub2e4.<\/li>\n \uad6c\ub450\uc810 \uc81c\uac70: \ud14d\uc2a4\ud2b8 \ub0b4\uc758 \uad6c\ub450\uc810\uc744 \uc81c\uac70\ud558\uc5ec \uc21c\uc218\ud55c \ub2e8\uc5b4\ub9cc \ub0a8\uae41\ub2c8\ub2e4.<\/li>\n \ubd88\uc6a9\uc5b4 \uc81c\uac70: \uc758\ubbf8 \uc5c6\ub294 \ubd88\uc6a9\uc5b4\ub97c \uc81c\uac70\ud558\uc5ec \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ub192\uc785\ub2c8\ub2e4.<\/li>\n \ud1a0\ud070\ud654: \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub610\ub294 n-gram\uc73c\ub85c \ubd84\ub9ac\ud558\uc5ec \ubd84\uc11d\ud569\ub2c8\ub2e4.<\/li>\n \uc5b4\uac04 \ucd94\ucd9c \ub610\ub294 \ud45c\uc81c\uc5b4 \ucd94\ucd9c: \ub2e8\uc5b4\uc758 \ud615\ud0dc\ub97c \uc904\uc5ec\uc8fc\ub294 \uacfc\uc815\uc744 \ud1b5\ud574 \ucc28\uc6d0 \ucd95\uc18c\ub97c \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n6. \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \uad6c\ud604<\/h2>\n\uc774\uc81c \uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud558\uc5ec \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\uc758 \ucf54\ub4dc\ub294 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378\uc758 \ud559\uc2b5 \uacfc\uc815\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/p>\n \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nimport nltk\nfrom nltk.corpus import stopwords\nimport string\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('spam_emails.csv')\n\n# \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac \ud568\uc218 \uc815\uc758\ndef preprocess_text(text):\n text = text.lower() # \uc18c\ubb38\uc790 \ubcc0\ud658\n text = text.translate(str.maketrans('', '', string.punctuation)) # \uad6c\ub450\uc810 \uc81c\uac70\n text = ' '.join([word for word in text.split() if word not in stopwords.words('english')]) # \ubd88\uc6a9\uc5b4 \uc81c\uac70\n return text\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ndata['processed_text'] = data['text'].apply(preprocess_text)\n\n# \ud6c8\ub828 \ub370\uc774\ud130\uc640 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\ub85c \ubd84\ub9ac\nX_train, X_test, y_train, y_test = train_test_split(data['processed_text'], data['label'], test_size=0.2)\n\n# \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130 \ubca1\ud130\ud654\nvectorizer = CountVectorizer()\nX_train_vectorized = vectorizer.fit_transform(X_train)\nX_test_vectorized = vectorizer.transform(X_test)\n\n# \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \ud559\uc2b5\nmodel = LogisticRegression()\nmodel.fit(X_train_vectorized, y_train)\n\n# \uc608\uce21 \uc218\ud589\ny_pred = model.predict(X_test_vectorized)\n\n# \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00\naccuracy = accuracy_score(y_test, y_pred)\nconf_matrix = confusion_matrix(y_test, y_pred)\nprint(f'\uc815\ud655\ub3c4: {accuracy}')\nprint(f'\ud63c\ub3d9 \ud589\ub82c:\\n {conf_matrix}')\n<\/code><\/pre>\n7. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc608\uce21\uc744 \uc218\ud589\ud558\uace0 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \uc704\uc758 \ucf54\ub4dc\uc5d0\uc11c\ub294 \uc815\ud655\ub3c4\uc640 \ud63c\ub3d9 \ud589\ub82c\uc744 \ud1b5\ud574 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud588\uc2b5\ub2c8\ub2e4. \uc774 \uc678\uc5d0\ub3c4 \uc815\ubc00\ub3c4, \uc7ac\ud604\uc728, F1 \uc810\uc218\uc640 \uac19\uc740 \ub2e4\uc591\ud55c \uba54\ud2b8\ub9ad\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n 8. \uacb0\uacfc \ud574\uc11d \ubc0f \uc801\uc6a9<\/h2>\n\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud55c \ud6c4, \uacb0\uacfc\ub97c \ud574\uc11d\ud558\uace0 \uc2e4\uc81c \uc560\ud50c\ub9ac\ucf00\uc774\uc158\uc5d0 \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud560 \uc218 \uc788\uc744\uc9c0 \uace0\ubbfc\ud558\ub294 \uac83\uc774 \uc911\uc694\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uc2a4\ud338 \ud544\ud130\ub9c1 \uc2dc\uc2a4\ud15c\uc5d0 \uc774 \ubaa8\ub378\uc744 \ud1b5\ud569\ud558\uc5ec \uc0ac\uc6a9\uc790\uac00 \uc2a4\ud338 \ub610\ub294 \uc911\uc694\ud55c \uc774\uba54\uc77c\uc744 \ud544\ud130\ub9c1\ud560 \uc218 \uc788\ub3c4\ub85d \ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc0ac\uc6a9\uc790 \uacbd\ud5d8\uc744 \uac1c\uc120\ud558\uace0 \uc774\uba54\uc77c \uad00\ub9ac\uc758 \ud6a8\uc728\uc131\uc744 \ub192\uc77c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n 9. \uacb0\ub860<\/h2>\n\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ucd08 \uac1c\ub150\uacfc \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \uc774\uc6a9\ud55c \uc2e4\uc2b5\uc744 \uc9c4\ud589\ud588\uc2b5\ub2c8\ub2e4. \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \ub2e4\uc591\ud55c \uc751\uc6a9 \ud504\ub85c\uadf8\ub7a8\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc73c\uba70, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub294 \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \uc720\uc6a9\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \uc55e\uc73c\ub85c \ub354 \ubc1c\uc804\ub41c \ub525 \ub7ec\ub2dd \ubaa8\ub378\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc744 \ud559\uc2b5\ud558\uc5ec \ubcf4\ub2e4 \ubcf5\uc7a1\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud560 \uc218 \uc788\ub3c4\ub85d \ub178\ub825\ud569\uc2dc\ub2e4.<\/p>\n 10. \ucc38\uace0 \uc790\ub8cc<\/h2>\n\ub354\uc6b1 \uae4a\uc774 \uc788\ub294 \ud559\uc2b5\uc744 \uc704\ud574 \uc544\ub798 \uc790\ub8cc\ub4e4\uc744 \ucc38\uace0\ud558\uc2dc\uba74 \uc88b\uc2b5\ub2c8\ub2e4.<\/p>\n \nCoursera: Natural Language Processing<\/a><\/li>\n Kaggle: Natural Language Processing<\/a><\/li>\n Towards Data Science: Introduction to Logistic Regression<\/a><\/li>\n<\/ul>\n<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \ucef4\ud4e8\ud130\uc640 \uc778\uac04 \uc5b8\uc5b4 \uac04\uc758 \uc0c1\ud638\uc791\uc6a9\uc744 \ud3ec\ud568\ud558\ub294 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 NLP \ubd84\uc57c\ub3c4 \ub9ce\uc740 \ubcc0\ud654\ub97c \uacaa\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ube48\ubc88\ud788 \uc0ac\uc6a9\ub418\ub294 \uae30\ucd08\uc801\uc778 \uae30\ubc95 \uc911 \ud558\ub098\ub85c, \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c … \ub354 \ubcf4\uae30 “\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc2e4\uc2b5”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[16],"tags":[],"class_list":["post-25235","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\n\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc2e4\uc2b5 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/25235\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc2e4\uc2b5 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub294 \ucef4\ud4e8\ud130\uc640 \uc778\uac04 \uc5b8\uc5b4 \uac04\uc758 \uc0c1\ud638\uc791\uc6a9\uc744 \ud3ec\ud568\ud558\ub294 \uc778\uacf5\uc9c0\ub2a5(AI)\uc758 \ud55c \ubd84\uc57c\uc785\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 \ub525 \ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 NLP \ubd84\uc57c\ub3c4 \ub9ce\uc740 \ubcc0\ud654\ub97c \uacaa\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0(Logistic Regression)\ub294 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0\uc11c \ube48\ubc88\ud788 \uc0ac\uc6a9\ub418\ub294 \uae30\ucd08\uc801\uc778 \uae30\ubc95 \uc911 \ud558\ub098\ub85c, \ud14d\uc2a4\ud2b8 \ubd84\ub958 \ubb38\uc81c\ub97c \ud574\uacb0\ud558\ub294 \ub370 \ub9e4\uc6b0 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uae30\ubcf8 \uac1c\ub150\uacfc \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c … \ub354 \ubcf4\uae30 "\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \uc2e4\uc2b5"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/25235\/\" \/>\n<meta property=\"og:site_name\" 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\uc790\uc5f0\uc5b4\ucc98\ub9ac","link":"https:\/\/atmokpo.com\/w\/category\/%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%9e%90%ec%97%b0%ec%96%b4%ec%b2%98%eb%a6%ac\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":25421,"url":"https:\/\/atmokpo.com\/w\/25421\/","url_meta":{"origin":25235,"position":5},"title":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, BERT(Bidirectional Encoder Representations from Transformers)","author":"root","date":"2024\ub144 10\uc6d4 26\uc77c","format":false,"excerpt":"\uc791\uc131\uc77c: [\ub0a0\uc9dc] \uc791\uc131\uc790: \uc870\uad11\ud615 1. \uc11c\ub860 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP, Natural Language Processing)\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud558\ub294 \ubd84\uc57c\ub85c, \ucd5c\uadfc \uba87 \ub144 \uac04 \uae09\uaca9\ud55c \ubc1c\uc804\uc744 \uc774\ub8e8\uc5c8\uc2b5\ub2c8\ub2e4. \uadf8 \uc911\uc2ec\uc5d0\ub294 \ub525 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