{"id":1751,"date":"2023-07-25T03:30:46","date_gmt":"2023-07-25T03:30:46","guid":{"rendered":"https:\/\/statorials.org\/id\/aic-dengan-python\/"},"modified":"2023-07-25T03:30:46","modified_gmt":"2023-07-25T03:30:46","slug":"aic-dengan-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/aic-dengan-python\/","title":{"rendered":"Cara menghitung aic model regresi dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Kriteria Informasi Akaike (AIC) adalah metrik yang digunakan untuk membandingkan kesesuaian model regresi yang berbeda.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dihitung sebagai berikut:<\/span><\/p>\n<p> <span style=\"color: #000000;\">AIC = 2K \u2013 2 <em>ln<\/em> (L)<\/span><\/p>\n<p> <span style=\"color: #000000;\">Emas:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><strong>K :<\/strong> Jumlah parameter model. Nilai default K adalah 2, sehingga model dengan hanya satu variabel prediktor akan memiliki nilai K 2+1 = 3.<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong><em>ln<\/em> (L)<\/strong> : Log-likelihood model. Ini memberi tahu kita probabilitas model, berdasarkan datanya.<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">AIC dirancang untuk menemukan model yang menjelaskan variasi data paling banyak, sekaligus memberi sanksi pada model yang menggunakan jumlah parameter berlebihan.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Setelah Anda memasang beberapa model regresi, Anda dapat membandingkan<\/span> <span style=\"color: #000000;\">nilai AIC setiap model. Model dengan AIC terendah memberikan kesesuaian terbaik.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Untuk menghitung AIC model regresi berganda dengan Python, kita dapat menggunakan fungsi <strong>statsmodels.regression.linear_model.OLS()<\/strong> , yang memiliki properti bernama <strong>aic<\/strong> yang memberi tahu kita nilai AIC untuk model tertentu.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Contoh berikut menunjukkan cara menggunakan fungsi ini untuk menghitung dan menafsirkan AIC untuk berbagai model regresi dengan Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Contoh: menghitung dan menafsirkan AIC dengan Python<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Katakanlah kita ingin menyesuaikan dua <a href=\"https:\/\/statorials.org\/id\/regresi-linier-berganda\/\" target=\"_blank\" rel=\"noopener\">model regresi linier berganda<\/a> yang berbeda menggunakan variabel dari dataset <strong>mtcars<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Pertama, kami akan memuat kumpulan data ini:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">linear_model<\/span> <span style=\"color: #008000;\">import<\/span> LinearRegression\n<span style=\"color: #008000;\">import<\/span> statsmodels. <span style=\"color: #3366ff;\">api<\/span> <span style=\"color: #008000;\">as<\/span> sm\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n\n<span style=\"color: #008080;\">#define URL where dataset is located\n<\/span>url = \"https:\/\/raw.githubusercontent.com\/Statorials\/Python-Guides\/main\/mtcars.csv\"\n\n<span style=\"color: #008080;\">#read in data\n<\/span>data = pd. <span style=\"color: #3366ff;\">read_csv<\/span> (url)\n\n<span style=\"color: #008080;\">#view head of data\n<\/span>data. <span style=\"color: #3366ff;\">head<\/span> ()\n\n        model mpg cyl disp hp drat wt qsec vs am gear carb\n0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4\n1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4\n2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1\n3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1\n4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Berikut variabel prediktor yang akan kami gunakan di setiap model:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Variabel prediktor pada model 1: disp, hp, wt, qsec<\/span><\/li>\n<li> <span style=\"color: #000000;\">Variabel prediktor pada model 2: disp, qsec<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Kode berikut menunjukkan cara menyesuaikan model pertama dan menghitung AIC:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define response variable\n<\/span>y = data['mpg']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = data[['disp', 'hp', 'wt', 'qsec']]\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view AIC of model\n<\/span><span style=\"color: #993300;\">print<\/span> (model. <span style=\"color: #3366ff;\">aic<\/span> )\n\n157.06960941462438<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">AIC model ini ternyata <strong>157,07<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita akan memasang model kedua dan menghitung AIC:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><strong><span style=\"color: #008080;\">#define response variable\n<\/span>y = data['mpg']\n\n<span style=\"color: #008080;\">#define predictor variables\n<\/span>x = data[['disp', 'qsec']]\n\n<span style=\"color: #008080;\">#add constant to predictor variables\n<\/span>x = sm. <span style=\"color: #3366ff;\">add_constant<\/span> (x)\n\n<span style=\"color: #008080;\">#fit regression model\n<\/span>model = sm. <span style=\"color: #3366ff;\">OLS<\/span> (y,x). <span style=\"color: #3366ff;\">fit<\/span> ()\n\n<span style=\"color: #008080;\">#view AIC of model\n<\/span><span style=\"color: #993300;\">print<\/span> (model. <span style=\"color: #3366ff;\">aic<\/span> )\n\n169.84184864154588<\/strong><\/span><\/pre>\n<p> <span style=\"color: #000000;\">AIC model ini ternyata <strong>169,84<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Karena model pertama memiliki nilai AIC yang lebih rendah, maka model tersebut merupakan model yang paling cocok.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Setelah kami mengidentifikasi model ini sebagai yang terbaik, kami dapat melanjutkan dengan penyesuaian model dan menganalisis hasilnya, termasuk nilai R-kuadrat dan koefisien beta, untuk menentukan hubungan yang tepat antara kumpulan variabel prediktif dan <a href=\"https:\/\/statorials.org\/id\/variabel-tanggapan-penjelas\/\" target=\"_blank\" rel=\"noopener\">variabel respons<\/a> .<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Sumber daya tambahan<\/strong><\/span><\/h3>\n<p> <a href=\"https:\/\/statorials.org\/id\/python-regresi-linier\/\" target=\"_blank\" rel=\"noopener\">Panduan Lengkap Regresi Linier dengan Python<\/a><br \/> <a href=\"https:\/\/statorials.org\/id\/r-persegi-dengan-python-menyesuaikan\/\" target=\"_blank\" rel=\"noopener\">Cara menghitung R-kuadrat yang disesuaikan dengan Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Kriteria Informasi Akaike (AIC) adalah metrik yang digunakan untuk membandingkan kesesuaian model regresi yang berbeda. Ini dihitung sebagai berikut: AIC = 2K \u2013 2 ln (L) Emas: K : Jumlah parameter model. Nilai default K adalah 2, sehingga model dengan hanya satu variabel prediktor akan memiliki nilai K 2+1 = 3. ln (L) : Log-likelihood [&hellip;]<\/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":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Cara menghitung AIC model regresi dengan Python<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menghitung nilai Akaike Information Criterion (AIC) model regresi dengan Python.\" \/>\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\/aic-dengan-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara menghitung AIC model regresi dengan Python\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menghitung nilai Akaike Information Criterion (AIC) model regresi dengan Python.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/aic-dengan-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T03:30:46+00:00\" \/>\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\/aic-dengan-python\/\",\"url\":\"https:\/\/statorials.org\/id\/aic-dengan-python\/\",\"name\":\"Cara menghitung AIC model regresi dengan Python\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-25T03:30:46+00:00\",\"dateModified\":\"2023-07-25T03:30:46+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menghitung nilai Akaike Information Criterion (AIC) model regresi dengan Python.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/aic-dengan-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/aic-dengan-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/aic-dengan-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara menghitung aic model regresi 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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