{"id":1649,"date":"2023-07-25T12:50:55","date_gmt":"2023-07-25T12:50:55","guid":{"rendered":"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/"},"modified":"2023-07-25T12:50:55","modified_gmt":"2023-07-25T12:50:55","slug":"pemasangan-kurva-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/","title":{"rendered":"Pemasangan kurva dengan python (dengan contoh)"},"content":{"rendered":"

<\/p>\n

\n

Seringkali Anda mungkin ingin menyesuaikan kurva ke kumpulan data dengan Python.<\/span> <\/p>\n

\"\"<\/p>\n

Contoh langkah demi langkah berikut menjelaskan cara menyesuaikan kurva ke data dengan Python menggunakan fungsi numpy.polyfit()<\/a> dan cara menentukan kurva mana yang paling sesuai dengan data.<\/span><\/p>\n

Langkah 1: Buat dan visualisasikan data<\/strong><\/span><\/h3>\n

Mari kita mulai dengan membuat kumpulan data palsu, lalu membuat plot sebar untuk memvisualisasikan datanya:<\/span> <\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15],\n                   ' y<\/span> ': [3, 14, 23, 25, 23, 15, 9, 5, 9, 13, 17, 24, 32, 36, 46]})\n\n#create scatterplot of x vs. y\n<\/span>plt. scatter<\/span> (df. x<\/span> , df. y<\/span> )<\/strong> <\/pre>\n
\"\"<\/p>\n
 Langkah 2: Sesuaikan Beberapa Kurva<\/strong><\/span><\/h3>\n
 Mari kita masukkan beberapa model regresi polinomial ke data dan visualisasikan kurva setiap model dalam plot yang sama:<\/span> <\/p>\n
 import<\/span> numpy as<\/span> np\n\n#fit polynomial models up to degree 5\n<\/span>model1 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 1))\nmodel2 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 2))\nmodel3 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 3))\nmodel4 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 4))\nmodel5 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 5))\n\n#create scatterplot\n<\/span>polyline = np. linspace<\/span> (1, 15, 50)\nplt. scatter<\/span> (df. x<\/span> , df. y<\/span> )\n\n#add fitted polynomial lines to scatterplot \n<\/span>plt. plot<\/span> (polyline, model1(polyline), color=' green<\/span> ')\nplt. plot<\/span> (polyline, model2(polyline), color=' red<\/span> ')\nplt. plot<\/span> (polyline, model3(polyline), color=' purple<\/span> ')\nplt. plot<\/span> (polyline, model4(polyline), color=' blue<\/span> ')\nplt. plot<\/span> (polyline, model5(polyline), color=' orange<\/span> ')\nplt. show<\/span> ()\n<\/span><\/span><\/strong><\/pre>\n
\"\"<\/p>\n
 Nilai ini menunjukkan persentase variasi variabel respon yang dapat dijelaskan oleh variabel prediktor dalam model, disesuaikan dengan jumlah variabel prediktor.<\/span><\/p>\n
 #define function to calculate adjusted r-squared\ndef<\/span> adjR(x, y, degree):\n    results = {}\n    coeffs = np. polyfit<\/span> (x, y, degree)\n    p = np. poly1d<\/span> (coeffs)\n    yhat = p(x)\n    ybar = np. sum<\/span> (y)\/len(y)\n    ssreg = np. sum<\/span> ((yhat-ybar)**2)\n    sstot = np. sum<\/span> ((y - ybar)**2)\n    results[' r_squared<\/span> '] = 1- (((1-(ssreg\/sstot))*(len(y)-1))\/(len(y)-degree-1))\n\n    return<\/span> results<\/span>\n\n#calculated adjusted R-squared of each model\n<\/span>adjR(df. x<\/span> , df. y<\/span> , 1)\nadjR(df. x<\/span> , df. y<\/span> , 2)\nadjR(df. x<\/span> , df. y<\/span> , 3)\nadjR(df. x<\/span> , df. y<\/span> , 4)\nadjR(df. x<\/span> , df. y<\/span> , 5)\n\n{'r_squared': 0.3144819}\n{'r_squared': 0.5186706}\n{'r_squared': 0.7842864}\n{'r_squared': 0.9590276}\n{'r_squared': 0.9549709}\n<\/strong><\/pre>\n
 Dari hasil tersebut terlihat bahwa model dengan Adjusted R-squared tertinggi adalah polinomial derajat keempat yang memiliki Adjusted R-squared sebesar 0,959<\/strong> .<\/span><\/p>\n
 Langkah 3: Visualisasikan kurva terakhir<\/strong><\/span><\/h3>\n
 Terakhir, kita dapat membuat plot sebar dengan kurva model polinomial derajat keempat:<\/span> <\/p>\n
 #fit fourth-degree polynomial\n<\/span>model4 = np. poly1d<\/span> (np. polyfit<\/span> (df. x<\/span> , df. y<\/span> , 4))\n\n#define scatterplot\n<\/span>polyline = np. linspace<\/span> (1, 15, 50)\nplt. scatter<\/span> (df. x<\/span> , df. y<\/span> )\n\n#add fitted polynomial curve to scatterplot\n<\/span>plt. plot<\/span> (polyline, model4(polyline), ' --<\/span> ', color=' red<\/span> ')\nplt. show<\/span> ()\n<\/strong><\/pre>\n
\"\"<\/p>\n
 Kita juga bisa mendapatkan persamaan untuk garis ini menggunakan fungsi print()<\/strong> :<\/span><\/p>\n
 print<\/span> (model4)\n\n          4 3 2\n-0.01924x + 0.7081x - 8.365x + 35.82x - 26.52\n<\/strong><\/pre>\n
 Persamaan kurvanya adalah sebagai berikut:<\/span><\/p>\n
 kamu = -0,01924x 4<\/sup> + 0,7081x 3<\/sup> \u2013 8,365x 2<\/sup> + 35,82x \u2013 26,52<\/span><\/p>\n
 Persamaan ini dapat kita gunakan untuk memprediksi nilai variabel respon<\/a> berdasarkan variabel prediktor dalam model. Misalnya jika x<\/em> = 4 maka kita prediksi y<\/em> = 23,32<\/strong> :<\/span><\/p>\n
 y = -0,0192(4) 4<\/sup> + 0,7081(4) 3<\/sup> \u2013 8,365(4) 2<\/sup> + 35,82(4) \u2013 26,52 = 23,32<\/span><\/p>\n
 Sumber daya tambahan<\/strong><\/span><\/h3>\n
 Pengantar Regresi Polinomial
<\/a> Cara melakukan regresi polinomial dengan Python<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
Seringkali Anda mungkin ingin menyesuaikan kurva ke kumpulan data dengan Python. Contoh langkah demi langkah berikut menjelaskan cara menyesuaikan kurva ke data dengan Python menggunakan fungsi numpy.polyfit() dan cara menentukan kurva mana yang paling sesuai dengan data. Langkah 1: Buat dan visualisasikan data Mari kita mulai dengan membuat kumpulan data palsu, lalu membuat plot sebar […]<\/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":"\nPemasangan Kurva dengan Python (dengan Contoh) - Statologi<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara menyesuaikan kurva dengan Python, dengan beberapa contoh.\" \/>\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\/pemasangan-kurva-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Pemasangan Kurva dengan Python (dengan Contoh) - Statologi\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara menyesuaikan kurva dengan Python, dengan beberapa contoh.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-25T12:50:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/courbepython3.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=\"3 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/\",\"url\":\"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/\",\"name\":\"Pemasangan Kurva dengan Python (dengan Contoh) - Statologi\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-25T12:50:55+00:00\",\"dateModified\":\"2023-07-25T12:50:55+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara menyesuaikan kurva dengan Python, dengan beberapa contoh.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/pemasangan-kurva-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Pemasangan kurva dengan python (dengan contoh)\"}]},{\"@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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 Untuk menentukan kurva mana yang paling sesuai dengan data, kita dapat melihat R kuadrat yang disesuaikan<\/a> dari setiap model.<\/span><\/p>\n