{"id":884,"date":"2023-07-28T10:44:58","date_gmt":"2023-07-28T10:44:58","guid":{"rendered":"https:\/\/statorials.org\/id\/python-regresi-polinomial\/"},"modified":"2023-07-28T10:44:58","modified_gmt":"2023-07-28T10:44:58","slug":"python-regresi-polinomial","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/python-regresi-polinomial\/","title":{"rendered":"Cara melakukan regresi polinomial dengan python"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">Analisis regresi digunakan untuk mengukur hubungan antara satu atau lebih variabel penjelas dan variabel respon.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Jenis analisis regresi yang paling umum adalah <a href=\"https:\/\/statorials.org\/id\/regresi-linier-1\/\" target=\"_blank\" rel=\"noopener noreferrer\">regresi linier sederhana<\/a> , digunakan ketika variabel prediktor dan variabel respons mempunyai hubungan linier.<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9521 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polynomialpython1.png\" alt=\"\" width=\"371\" height=\"249\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Namun terkadang hubungan antara variabel prediktor dan variabel respon bersifat nonlinier.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Misalnya, hubungan sebenarnya mungkin berbentuk kuadrat:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-9522 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polynomialpython2.png\" alt=\"\" width=\"369\" height=\"256\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Atau bisa juga kubik:<\/span> <\/p>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9523 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polynomialpython3.png\" alt=\"\" width=\"382\" height=\"258\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Dalam kasus ini, masuk akal untuk menggunakan <strong>regresi polinomial<\/strong> , yang dapat menjelaskan hubungan nonlinier antar variabel.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tutorial ini menjelaskan cara melakukan regresi polinomial dengan Python.<\/span><\/p>\n<h2> <span style=\"color: #000000;\"><strong>Contoh: Regresi Polinomial dengan Python<\/strong><\/span><\/h2>\n<p> <span style=\"color: #000000;\">Misalkan kita memiliki variabel prediktor (x) dan variabel respon (y) berikut dengan Python:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>x = [2, 3, 4, 5, 6, 7, 7, 8, 9, 11, 12]\ny = [18, 16, 15, 17, 20, 23, 25, 28, 31, 30, 29]\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Jika kita membuat diagram sebar sederhana dari data ini, kita dapat melihat bahwa hubungan antara x dan y jelas tidak linier:<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> matplotlib.pyplot <span style=\"color: #107d3f;\">as<\/span> plt\n\n<span style=\"color: #008080;\">#create scatterplot<\/span> \nplt.scatter(x, y)\n<\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"wp-image-9524 aligncenter\" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polynomialpython4.png\" alt=\"\" width=\"383\" height=\"257\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Oleh karena itu, tidak masuk akal untuk menyesuaikan model regresi linier dengan data ini. Sebagai gantinya, kita dapat mencoba menyesuaikan model regresi polinomial dengan derajat 3 menggunakan fungsi <a href=\"https:\/\/numpy.org\/doc\/stable\/reference\/generated\/numpy.polyfit.html\" target=\"_blank\" rel=\"noopener noreferrer\">numpy.polyfit()<\/a> :<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n\n<span style=\"color: #008080;\">#polynomial fit with degree = 3\n<\/span>model = np.poly1d(np.polyfit(x, y, 3))\n\n<span style=\"color: #008080;\">#add fitted polynomial line to scatterplot\n<\/span>polyline = np.linspace(1, 12, 50)\nplt.scatter(x, y)\nplt.plot(polyline, model(polyline))\nplt.show()<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-9525 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polynomialpython5.png\" alt=\"Garis Regresi Polinomial dengan Python\" width=\"379\" height=\"249\" srcset=\"\" sizes=\"\"><\/p>\n<p> <span style=\"color: #000000;\">Kita dapat memperoleh persamaan regresi polinomial yang sesuai dengan mencetak koefisien model:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>print(model)\n\npoly1d([ -0.10889554, 2.25592957, -11.83877127, 33.62640038])\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Persamaan regresi polinomial yang dipasang adalah:<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>kamu = -0,109x <sup>3<\/sup> + 2,256x <sup>2<\/sup> \u2013 11,839x + 33,626<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">Persamaan ini dapat digunakan untuk mencari nilai yang diharapkan dari variabel respon jika diberi nilai variabel penjelas tertentu.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Misalnya, asumsikan x = 4. Nilai yang diharapkan untuk variabel respon, y, adalah:<\/span><\/p>\n<p> <span style=\"color: #000000;\">kamu = -0,109(4) <sup>3<\/sup> + 2,256(4) <sup>2<\/sup> \u2013 11,839(4) + 33,626= <b>15,39<\/b> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat menuliskan fungsi singkat untuk mendapatkan R-squared model, yaitu proporsi varians variabel respon yang dapat dijelaskan oleh variabel prediktor.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define function to calculate r-squared<\/span>\n<span style=\"color: #008000;\">def<\/span> polyfit(x, y, degree):\n    results = {}\n    coeffs = numpy.polyfit(x, y, degree)\n    p = numpy.poly1d(coeffs)\n    <span style=\"color: #008080;\">#calculate r-squared<\/span>\n    yhat = p(x)\n    ybar = numpy.sum(y)\/len(y)\n    ssreg = numpy.sum((yhat-ybar)**2)\n    sstot = numpy.sum((y - ybar)**2)\n    results['r_squared'] = ssreg \/ sstot\n\n    <span style=\"color: #008000;\">return<\/span> results\n\n<span style=\"color: #008080;\">#find r-squared of polynomial model with degree = 3\n<\/span>polyfit(x, y, 3)\n\n{'r_squared': 0.9841113454245183}\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Dalam contoh ini, R kuadrat model adalah <strong>0,9841<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Artinya <strong>98,41%<\/strong> variasi variabel respon dapat dijelaskan oleh variabel prediktor.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Analisis regresi digunakan untuk mengukur hubungan antara satu atau lebih variabel penjelas dan variabel respon. Jenis analisis regresi yang paling umum adalah regresi linier sederhana , digunakan ketika variabel prediktor dan variabel respons mempunyai hubungan linier. Namun terkadang hubungan antara variabel prediktor dan variabel respon bersifat nonlinier. Misalnya, hubungan sebenarnya mungkin berbentuk kuadrat: Atau bisa [&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 Melakukan Regresi Polinomial dengan Python - Statologi<\/title>\n<meta name=\"description\" content=\"Penjelasan sederhana tentang cara melakukan regresi polinomial 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\/python-regresi-polinomial\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cara Melakukan Regresi Polinomial dengan Python - Statologi\" \/>\n<meta property=\"og:description\" content=\"Penjelasan sederhana tentang cara melakukan regresi polinomial dengan Python.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/python-regresi-polinomial\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-28T10:44:58+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polynomialpython1.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\/python-regresi-polinomial\/\",\"url\":\"https:\/\/statorials.org\/id\/python-regresi-polinomial\/\",\"name\":\"Cara Melakukan Regresi Polinomial dengan Python - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-28T10:44:58+00:00\",\"dateModified\":\"2023-07-28T10:44:58+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Penjelasan sederhana tentang cara melakukan regresi polinomial dengan Python.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/python-regresi-polinomial\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/python-regresi-polinomial\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/python-regresi-polinomial\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Cara melakukan regresi polinomial 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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