{"id":1169,"date":"2023-07-27T10:29:04","date_gmt":"2023-07-27T10:29:04","guid":{"rendered":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/"},"modified":"2023-07-27T10:29:04","modified_gmt":"2023-07-27T10:29:04","slug":"analisis-diskriminan-kuadrat-dengan-python","status":"publish","type":"post","link":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/","title":{"rendered":"Analisis diskriminan kuadrat dengan python (langkah demi langkah)"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><a href=\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat\/\" target=\"_blank\" rel=\"noopener noreferrer\">Analisis diskriminan kuadrat<\/a> adalah metode yang dapat Anda gunakan ketika Anda memiliki sekumpulan variabel prediktor dan Anda ingin mengklasifikasikan <a href=\"https:\/\/statorials.org\/id\/variabel-tanggapan-penjelas\/\" target=\"_blank\" rel=\"noopener noreferrer\">variabel respons<\/a> ke dalam dua kelas atau lebih.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Ini dianggap setara dengan <a href=\"https:\/\/statorials.org\/id\/analisis-diskriminan-linier-dengan-python\/\" target=\"_blank\" rel=\"noopener noreferrer\">analisis diskriminan linier<\/a> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Tutorial ini memberikan contoh langkah demi langkah tentang cara melakukan analisis diskriminan kuadrat dengan Python.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 1: Muat Perpustakaan yang Diperlukan<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Pertama, kita akan memuat fungsi dan perpustakaan yang diperlukan untuk contoh ini:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <span style=\"color: #000000;\"><b><span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> train_test_split\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> RepeatedStratifiedKFold\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">model_selection<\/span> <span style=\"color: #008000;\">import<\/span> cross_val_score\n<span style=\"color: #008000;\">from<\/span> sklearn. <span style=\"color: #3366ff;\">discriminant_analysis<\/span> <span style=\"color: #008000;\">import<\/span> QuadraticDiscriminantAnalysis \n<span style=\"color: #008000;\">from<\/span> sklearn <span style=\"color: #008000;\">import<\/span> datasets\n<span style=\"color: #008000;\">import<\/span> matplotlib. <span style=\"color: #3366ff;\">pyplot<\/span> <span style=\"color: #008000;\">as<\/span> plt\n<span style=\"color: #008000;\">import<\/span> pandas <span style=\"color: #008000;\">as<\/span> pd\n<span style=\"color: #008000;\">import<\/span> numpy <span style=\"color: #008000;\">as<\/span> np<\/b><\/span><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 2: Muat data<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kita akan menggunakan dataset <strong>iris<\/strong> dari perpustakaan sklearn. Kode berikut menunjukkan cara memuat kumpulan data ini dan mengonversinya menjadi DataFrame pandas untuk kemudahan penggunaan:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load <em>iris<\/em> dataset<\/span>\niris = datasets. <span style=\"color: #3366ff;\">load_iris<\/span> ()\n\n<span style=\"color: #008080;\">#convert dataset to pandas DataFrame\n<\/span>df = pd.DataFrame(data = np.c_[iris[' <span style=\"color: #008000;\">data<\/span> '], iris[' <span style=\"color: #008000;\">target<\/span> ']],\n                 columns = iris[' <span style=\"color: #008000;\">feature_names<\/span> '] + [' <span style=\"color: #008000;\">target<\/span> '])\ndf[' <span style=\"color: #008000;\">species<\/span> '] = pd. <span style=\"color: #3366ff;\">Categorical<\/span> . <span style=\"color: #3366ff;\">from_codes<\/span> (iris.target, iris.target_names)\ndf.columns = [' <span style=\"color: #008000;\">s_length<\/span> ', ' <span style=\"color: #008000;\">s_width<\/span> ', ' <span style=\"color: #008000;\">p_length<\/span> ', ' <span style=\"color: #008000;\">p_width<\/span> ', ' <span style=\"color: #008000;\">target<\/span> ', ' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#view first six rows of DataFrame\n<\/span>df. <span style=\"color: #3366ff;\">head<\/span> ()\n\n   s_length s_width p_length p_width target species\n0 5.1 3.5 1.4 0.2 0.0 setosa\n1 4.9 3.0 1.4 0.2 0.0 setosa\n2 4.7 3.2 1.3 0.2 0.0 setosa\n3 4.6 3.1 1.5 0.2 0.0 setosa\n4 5.0 3.6 1.4 0.2 0.0 setosa\n\n<span style=\"color: #3366ff;\"><span style=\"color: #008080;\">#find how many total observations are in dataset<\/span>\n<span style=\"color: #000000;\">len(df.index)\n\n150<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat melihat bahwa dataset tersebut berisi total 150 observasi.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kita akan membangun model analisis diskriminan kuadrat untuk mengklasifikasikan spesies yang dimiliki suatu bunga.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kami akan menggunakan variabel prediktor berikut dalam model:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">Panjang sepal<\/span><\/li>\n<li> <span style=\"color: #000000;\">Lebar sepal<\/span><\/li>\n<li> <span style=\"color: #000000;\">Panjang kelopak<\/span><\/li>\n<li> <span style=\"color: #000000;\">Lebar kelopak<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">Dan kita akan menggunakannya untuk memprediksi variabel respon <em>Spesies<\/em> , yang mendukung tiga kelas potensial berikut:<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\">setosa<\/span><\/li>\n<li> <span style=\"color: #000000;\">versikolor<\/span><\/li>\n<li> <span style=\"color: #000000;\">Virginia<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 3: Sesuaikan model QDA<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Selanjutnya, kita akan menyesuaikan model QDA ke data kita menggunakan fungsi <a href=\"https:\/\/scikit-learn.org\/stable\/modules\/generated\/sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.html\" target=\"_blank\" rel=\"noopener noreferrer\">QuadraticDiscriminantAnalsys<\/a> sklearn:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define predictor and response variables\n<\/span>X = df[[' <span style=\"color: #008000;\">s_length<\/span> ',' <span style=\"color: #008000;\">s_width<\/span> ',' <span style=\"color: #008000;\">p_length<\/span> ',' <span style=\"color: #008000;\">p_width<\/span> ']]\ny = df[' <span style=\"color: #008000;\">species<\/span> ']\n\n<span style=\"color: #008080;\">#Fit the QDA model\n<\/span>model = QuadraticDiscriminantAnalysis()\nmodel. <span style=\"color: #3366ff;\">fit<\/span> (x,y)\n<\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>Langkah 4: Gunakan model untuk membuat prediksi<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Setelah kami memasang model menggunakan data kami, kami dapat mengevaluasi performa model menggunakan validasi silang k-fold bertingkat yang berulang.<\/span><\/p>\n<p> <span style=\"color: #000000;\">Untuk contoh ini, kita akan menggunakan 10 lipatan dan 3 pengulangan:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#Define method to evaluate model\n<span style=\"color: #000000;\">cv = RepeatedStratifiedKFold(n_splits= <span style=\"color: #008000;\">10<\/span> , n_repeats= <span style=\"color: #008000;\">3<\/span> , random_state= <span style=\"color: #008000;\">1<\/span> )\n<\/span>\n#evaluate model\n<span style=\"color: #000000;\">scores = cross_val_score(model, X, y, scoring=' <span style=\"color: #008000;\">accuracy<\/span> ', cv=cv, n_jobs=-1)\nprint( <span style=\"color: #3366ff;\">np.mean<\/span> (scores))<\/span>  \n\n<span style=\"color: #000000;\">0.97333333333334<\/span><\/span><\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita dapat melihat bahwa model tersebut mencapai akurasi rata-rata sebesar <strong>97,33%<\/strong> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Kita juga dapat menggunakan model ini untuk memprediksi kelas mana yang dimiliki bunga baru, berdasarkan nilai masukan:<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#define new observation\n<\/span>new = [5, 3, 1, .4]\n\n<span style=\"color: #008080;\">#predict which class the new observation belongs to\n<\/span>model. <span style=\"color: #3366ff;\">predict<\/span> ([new])\n\narray(['setosa'], dtype='&lt;U10')\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">Kita melihat bahwa model tersebut memperkirakan bahwa pengamatan baru ini milik spesies yang disebut <em>setosa<\/em> .<\/span><\/p>\n<p> <span style=\"color: #000000;\">Anda dapat menemukan kode Python lengkap yang digunakan dalam tutorial ini <a href=\"https:\/\/github.com\/Statorials\/Python-Guides\/blob\/main\/quadratic_discriminant_analysis.py\" target=\"_blank\" rel=\"noopener noreferrer\">di sini<\/a> .<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Analisis diskriminan kuadrat adalah metode yang dapat Anda gunakan ketika Anda memiliki sekumpulan variabel prediktor dan Anda ingin mengklasifikasikan variabel respons ke dalam dua kelas atau lebih. Ini dianggap setara dengan analisis diskriminan linier . Tutorial ini memberikan contoh langkah demi langkah tentang cara melakukan analisis diskriminan kuadrat dengan Python. Langkah 1: Muat Perpustakaan yang [&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>Analisis Diskriminan Kuadrat dengan Python (Langkah demi Langkah)<\/title>\n<meta name=\"description\" content=\"Tutorial ini menjelaskan cara melakukan analisis diskriminan kuadrat dengan Python, termasuk 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\/analisis-diskriminan-kuadrat-dengan-python\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analisis Diskriminan Kuadrat dengan Python (Langkah demi Langkah)\" \/>\n<meta property=\"og:description\" content=\"Tutorial ini menjelaskan cara melakukan analisis diskriminan kuadrat dengan Python, termasuk contoh langkah demi langkah.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/\" \/>\n<meta property=\"og:site_name\" content=\"Statorials\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-27T10:29:04+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=\"3 menit\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/\",\"url\":\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/\",\"name\":\"Analisis Diskriminan Kuadrat dengan Python (Langkah demi Langkah)\",\"isPartOf\":{\"@id\":\"https:\/\/statorials.org\/id\/#website\"},\"datePublished\":\"2023-07-27T10:29:04+00:00\",\"dateModified\":\"2023-07-27T10:29:04+00:00\",\"author\":{\"@id\":\"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81\"},\"description\":\"Tutorial ini menjelaskan cara melakukan analisis diskriminan kuadrat dengan Python, termasuk contoh langkah demi langkah.\",\"breadcrumb\":{\"@id\":\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/#breadcrumb\"},\"inLanguage\":\"id\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/statorials.org\/id\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Analisis diskriminan kuadrat dengan python (langkah demi langkah)\"}]},{\"@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. Dengan pengalaman dan keahlian yang luas di bidang statistika, saya ingin berbagi ilmu untuk memberdayakan mahasiswa melalui Statorials. Baca selengkapnya\",\"sameAs\":[\"http:\/\/statorials.org\/id\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Analisis Diskriminan Kuadrat dengan Python (Langkah demi Langkah)","description":"Tutorial ini menjelaskan cara melakukan analisis diskriminan kuadrat dengan Python, termasuk contoh langkah demi langkah.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/","og_locale":"id_ID","og_type":"article","og_title":"Analisis Diskriminan Kuadrat dengan Python (Langkah demi Langkah)","og_description":"Tutorial ini menjelaskan cara melakukan analisis diskriminan kuadrat dengan Python, termasuk contoh langkah demi langkah.","og_url":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/","og_site_name":"Statorials","article_published_time":"2023-07-27T10:29:04+00:00","author":"Benjamin anderson","twitter_card":"summary_large_image","twitter_misc":{"Ditulis oleh":"Benjamin anderson","Estimasi waktu membaca":"3 menit"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/","url":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/","name":"Analisis Diskriminan Kuadrat dengan Python (Langkah demi Langkah)","isPartOf":{"@id":"https:\/\/statorials.org\/id\/#website"},"datePublished":"2023-07-27T10:29:04+00:00","dateModified":"2023-07-27T10:29:04+00:00","author":{"@id":"https:\/\/statorials.org\/id\/#\/schema\/person\/3d17a1160dd2d052b7c78e502cb9ec81"},"description":"Tutorial ini menjelaskan cara melakukan analisis diskriminan kuadrat dengan Python, termasuk contoh langkah demi langkah.","breadcrumb":{"@id":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/#breadcrumb"},"inLanguage":"id","potentialAction":[{"@type":"ReadAction","target":["https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/statorials.org\/id\/analisis-diskriminan-kuadrat-dengan-python\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/statorials.org\/id\/"},{"@type":"ListItem","position":2,"name":"Analisis diskriminan kuadrat dengan python (langkah demi langkah)"}]},{"@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. Dengan pengalaman dan keahlian yang luas di bidang statistika, saya ingin berbagi ilmu untuk memberdayakan mahasiswa melalui Statorials. Baca selengkapnya","sameAs":["http:\/\/statorials.org\/id"]}]}},"yoast_meta":{"yoast_wpseo_title":"","yoast_wpseo_metadesc":"","yoast_wpseo_canonical":""},"_links":{"self":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts\/1169"}],"collection":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/comments?post=1169"}],"version-history":[{"count":0,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/posts\/1169\/revisions"}],"wp:attachment":[{"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/media?parent=1169"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/categories?post=1169"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/statorials.org\/id\/wp-json\/wp\/v2\/tags?post=1169"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}