{"id":2237,"date":"2023-07-23T03:24:57","date_gmt":"2023-07-23T03:24:57","guid":{"rendered":"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/"},"modified":"2023-07-23T03:24:57","modified_gmt":"2023-07-23T03:24:57","slug":"pengkodean-panas-dengan-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/","title":{"rendered":"Cara melakukan encoding one-hot dengan python"},"content":{"rendered":"

<\/p>\n

\n

Enkode one-hot<\/strong> digunakan untuk mengonversi variabel kategori ke dalam format yang dapat dengan mudah digunakan oleh algoritme pembelajaran mesin .<\/span><\/p>\n

Ide dasar dari one-hot coding adalah membuat variabel baru yang mengambil nilai 0 dan 1 untuk mewakili nilai kategorikal aslinya.<\/span><\/p>\n

Misalnya, gambar berikut menunjukkan bagaimana kita melakukan enkode one-hot untuk mengonversi variabel kategorikal yang berisi nama tim menjadi variabel baru yang hanya berisi nilai 0 dan 1:<\/span> <\/p>\n

\"\"<\/p>\n

Contoh langkah demi langkah berikut menunjukkan cara melakukan enkode one-hot untuk kumpulan data persis ini dengan Python.<\/span><\/p>\n

Langkah 1: Buat datanya<\/strong><\/span><\/h3>\n

Pertama, mari buat DataFrame panda berikut:<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'B', 'B', 'B', 'B', 'C', 'C'],\n                   ' points<\/span> ': [25, 12, 15, 14, 19, 23, 25, 29]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n  team points\n0 to 25\n1 to 12\n2 B 15\n3 B 14\n4 B 19\n5 B 23\n6 C 25\n7 C 29<\/strong><\/pre>\n
 Langkah 2: Lakukan pengkodean one-hot<\/strong><\/span><\/h3>\n
 Selanjutnya, mari kita impor fungsi OneHotEncoder()<\/strong> dari perpustakaan sklearn<\/strong> dan menggunakannya untuk melakukan pengkodean panas pada variabel ‘tim’ di pandas DataFrame:<\/span><\/p>\n
 from<\/span> sklearn. preprocessing<\/span> import<\/span> OneHotEncoder\n\n#creating instance of one-hot-encoder\n<\/span>encoder = OneHotEncoder(handle_unknown=' ignore<\/span> ')\n\n#perform one-hot encoding on 'team' column \n<\/span>encoder_df = pd. DataFrame<\/span> ( encoder.fit_transform<\/span> (df[[' team<\/span> ']]). toarray<\/span> ())\n\n#merge one-hot encoded columns back with original DataFrame\n<\/span>final_df = df. join<\/span> (encoder_df)\n\n#view final df\n<\/span>print<\/span> (final_df)\n\n  team points 0 1 2\n0 to 25 1.0 0.0 0.0\n1 to 12 1.0 0.0 0.0\n2 B 15 0.0 1.0 0.0\n3 B 14 0.0 1.0 0.0\n4 B 19 0.0 1.0 0.0\n5 B 23 0.0 1.0 0.0\n6 C 25 0.0 0.0 1.0\n7 C 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 Perhatikan bahwa tiga kolom baru telah ditambahkan ke DataFrame karena kolom “tim” asli berisi tiga nilai unik.<\/span><\/p>\n
 Catatan<\/strong> : Anda dapat menemukan dokumentasi lengkap untuk fungsi OneHotEncoder()<\/strong> di sini<\/a> .<\/span><\/p>\n
 Langkah 3: Hapus variabel kategori asli<\/strong><\/span><\/h3>\n
 Terakhir, kita dapat menghapus variabel ‘tim’ asli dari DataFrame karena kita tidak lagi membutuhkannya:<\/span><\/p>\n
 #drop 'team' column\n<\/span>final_df. drop<\/span> (' team<\/span> ', axis= 1<\/span> , inplace= True<\/span> )\n\n#view final df\n<\/span>print<\/span> (final_df)\n\n   points 0 1 2\n0 25 1.0 0.0 0.0\n1 12 1.0 0.0 0.0\n2 15 0.0 1.0 0.0\n3 14 0.0 1.0 0.0\n4 19 0.0 1.0 0.0\n5 23 0.0 1.0 0.0\n6 25 0.0 0.0 1.0\n7 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 Kita juga dapat mengganti nama kolom DataFrame akhir agar lebih mudah dibaca:<\/span><\/p>\n
 #rename columns\n<\/span>final_df. columns<\/span> = ['points', 'teamA', 'teamB', 'teamC']\n\n#view final df<\/span>\nprint<\/span> (final_df)\n\n   points teamA teamB teamC\n0 25 1.0 0.0 0.0\n1 12 1.0 0.0 0.0\n2 15 0.0 1.0 0.0\n3 14 0.0 1.0 0.0\n4 19 0.0 1.0 0.0\n5 23 0.0 1.0 0.0\n6 25 0.0 0.0 1.0\n7 29 0.0 0.0 1.0\n<\/strong><\/pre>\n
 Encoding one-hot selesai dan sekarang kita dapat memasukkan DataFrame pandas ini ke dalam algoritma pembelajaran mesin apa pun yang kita inginkan.<\/span><\/p>\n
 Sumber daya tambahan<\/strong><\/span><\/h3>\n
 Cara menghitung mean terpangkas dengan Python<\/a>
 Cara melakukan regresi linier dengan Python<\/a>
 Cara Melakukan Regresi Logistik dengan Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Enkode one-hot digunakan untuk mengonversi variabel kategori ke dalam format yang dapat dengan mudah digunakan oleh algoritme pembelajaran mesin . Ide dasar dari one-hot coding adalah membuat variabel baru yang mengambil nilai 0 dan 1 untuk mewakili nilai kategorikal aslinya. Misalnya, gambar berikut menunjukkan bagaimana kita melakukan enkode one-hot untuk mengonversi variabel kategorikal yang berisi […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"yoast_head":"\nCara Melakukan One-Hot Encoding dengan Python - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara melakukan pengkodean one-hot dengan Python, dengan contoh langkah demi langkah.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Melakukan One-Hot Encoding dengan Python - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara melakukan pengkodean one-hot dengan Python, dengan contoh langkah demi langkah.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-23T03:24:57+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/unhot1.png\" \/>\n<meta name=\"author\" content=\"Benjamin anderson\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Ditulis oleh\" \/>\n\t<meta name=\"twitter:data1\" content=\"Benjamin anderson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimasi waktu membaca\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/\",\"url\":\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/\",\"name\":\"Cara Melakukan One-Hot Encoding dengan Python - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-23T03:24:57+00:00\",\"dateModified\":\"2023-07-23T03:24:57+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara melakukan pengkodean one-hot dengan Python, dengan contoh langkah demi langkah.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/pengkodean-panas-dengan-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara melakukan encoding one-hot dengan python\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/statorials.org\/id\/#website\",\"url\":\"https:\/\/statorials.org\/id\/\",\"name\":\"Statorials\",\"description\":\"Panduan anda untuk kompetensi statistik!\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/statorials.org\/id\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"id\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\",\"name\":\"Benjamin anderson\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"id\",\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/image\/\",\"url\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"contentUrl\":\"http:\/\/statorials.org\/id\/wp-content\/uploads\/2023\/10\/Dr.-Benjamin-Anderson-96x96.jpg\",\"caption\":\"Benjamin anderson\"},\"description\":\"Halo, saya Benjamin, pensiunan profesor statistika yang menjadi guru Statorial yang berdedikasi. 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