{"id":478465,"date":"2023-08-09T09:33:12","date_gmt":"2023-08-09T09:33:12","guid":{"rendered":""},"modified":"2023-09-05T11:16:48","modified_gmt":"2023-09-05T11:16:48","slug":"polynomial-regression","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/id\/wiki\/polynomial-regression\/","title":{"rendered":"Regresi polinomial"},"content":{"rendered":"<p>Regresi polinomial adalah salah satu jenis analisis regresi dalam statistik yang berhubungan dengan pemodelan hubungan antara variabel independen <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>X<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">X<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.07847em;\">X<\/span><\/span><\/span><\/span><\/span> dan variabel terikat <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>kamu<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">kamu<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">kamu<\/span><\/span><\/span><\/span><\/span> sebagai polinomial derajat ke-n. Berbeda dengan regresi linier, yang memodelkan hubungan sebagai garis lurus, regresi polinomial menyesuaikan kurva dengan titik data, sehingga memberikan kecocokan yang lebih fleksibel.<\/p>\n<h2>Sejarah Asal Usul Regresi Polinomial dan Penyebutan Pertama Kalinya<\/h2>\n<p>Regresi polinomial berakar pada bidang interpolasi polinomial yang lebih luas, yang berasal dari karya matematika Isaac Newton dan Carl Friedrich Gauss. Metode interpolasi polinomial Newton dikembangkan pada akhir abad ke-17 dan merupakan salah satu teknik paling awal untuk menyesuaikan kurva polinomial ke titik data.<\/p>\n<p>Dalam konteks analisis regresi, regresi polinomial mulai mendapatkan daya tarik pada abad ke-20 seiring dengan kemajuan alat komputasi, yang memungkinkan pemodelan hubungan antar variabel yang lebih kompleks.<\/p>\n<h2>Informasi Lengkap tentang Regresi Polinomial. Memperluas Topik Regresi Polinomial<\/h2>\n<p>Regresi polinomial memperluas regresi linier sederhana dengan memungkinkan hubungan antara variabel bebas dan variabel terikat dimodelkan sebagai persamaan polinomial dengan bentuk:<br \/>\n<span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>kamu<\/mi><mo>=<\/mo><msub><mi>\u03b2<\/mi><mn>0<\/mn><\/msub><mo>+<\/mo><msub><mi>\u03b2<\/mi><mn>1<\/mn><\/msub><mi>X<\/mi><mo>+<\/mo><msub><mi>\u03b2<\/mi><mn>2<\/mn><\/msub><msup><mi>X<\/mi><mn>2<\/mn><\/msup><mo>+<\/mo><mo>\u2026<\/mo><mo>+<\/mo><msub><mi>\u03b2<\/mi><mi>N<\/mi><\/msub><msup><mi>X<\/mi><mi>N<\/mi><\/msup><mo>+<\/mo><mi>\u03f5<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">y = beta_0 + beta_1 x + beta_2 x^2 + ldots + beta_n x^n + epsilon<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">kamu<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><span class=\"mrel\">=<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3011em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">0<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3011em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">1<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord mathnormal\">X<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1.0085em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3011em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\"><span class=\"mord mathnormal\">X<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.8141em;\"><span style=\"top: -3.063em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mtight\">2<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6667em; vertical-align: -0.0833em;\"><\/span><span class=\"minner\">\u2026<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.1514em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">N<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mord\"><span class=\"mord mathnormal\">X<\/span><span class=\"msupsub\"><span class=\"vlist-t\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.6644em;\"><span style=\"top: -3.063em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">N<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><span class=\"mbin\">+<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em;\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">\u03f5<\/span><\/span><\/span><\/span><\/span><\/p>\n<h3>Penjelasan Persamaan:<\/h3>\n<ul>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>kamu<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">kamu<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">kamu<\/span><\/span><\/span><\/span><\/span>: Variabel tak bebas<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><msub><mi>\u03b2<\/mi><mi>Saya<\/mi><\/msub><\/mrow><annotation encoding=\"application\/x-tex\">beta_i<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.8889em; vertical-align: -0.1944em;\"><\/span><span class=\"mord\"><span class=\"mord mathnormal\" style=\"margin-right: 0.05278em;\">\u03b2<\/span><span class=\"msupsub\"><span class=\"vlist-t vlist-t2\"><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.3117em;\"><span style=\"top: -2.55em; margin-left: -0.0528em; margin-right: 0.05em;\"><span class=\"pstrut\" style=\"height: 2.7em;\"><\/span><span class=\"sizing reset-size6 size3 mtight\"><span class=\"mord mathnormal mtight\">Saya<\/span><\/span><\/span><\/span><span class=\"vlist-s\">\u200b<\/span><\/span><span class=\"vlist-r\"><span class=\"vlist\" style=\"height: 0.15em;\"><span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/span>: Koefisien<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>X<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">X<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">X<\/span><\/span><\/span><\/span><\/span>: Variabel bebas<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>\u03f5<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">epsilon<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">\u03f5<\/span><\/span><\/span><\/span><\/span>: Istilah kesalahan<\/li>\n<li><span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>N<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">N<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.4306em;\"><\/span><span class=\"mord mathnormal\">N<\/span><\/span><\/span><\/span><\/span>: Derajat polinomial<\/li>\n<\/ul>\n<p>Dengan menyesuaikan persamaan polinomial ke data, model dapat menangkap hubungan nonlinier dan memberikan pemahaman yang lebih mendalam tentang pola yang mendasari data.<\/p>\n<h2>Struktur Internal Regresi Polinomial. Cara Kerja Regresi Polinomial<\/h2>\n<p>Regresi polinomial bekerja dengan mencari koefisien yang meminimalkan jumlah selisih kuadrat antara nilai yang diamati dan nilai yang diprediksi oleh model polinomial. Proses ini biasa dilakukan melalui metode kuadrat terkecil.<\/p>\n<h3>Langkah-langkah dalam Regresi Polinomial:<\/h3>\n<ol>\n<li><strong>Pilih Derajat Polinomial<\/strong>: Derajat polinomial harus dipilih berdasarkan hubungan yang mendasari data.<\/li>\n<li><strong>Transformasikan Data<\/strong>: Membuat fitur polinomial untuk derajat yang dipilih.<\/li>\n<li><strong>Sesuaikan Modelnya<\/strong>: Memanfaatkan teknik regresi linier untuk mencari koefisien yang meminimalkan kesalahan.<\/li>\n<li><strong>Evaluasi Modelnya<\/strong>: Menilai kecocokan model menggunakan metrik seperti R-squared, mean squared error, dll.<\/li>\n<\/ol>\n<h2>Analisis Fitur Utama Regresi Polinomial<\/h2>\n<ul>\n<li><strong>Fleksibilitas<\/strong>: Dapat memodelkan hubungan nonlinier.<\/li>\n<li><strong>Kesederhanaan<\/strong>: Memperluas regresi linier dan dapat diselesaikan dengan teknik linier.<\/li>\n<li><strong>Risiko Overfitting<\/strong>: Polinomial derajat yang lebih tinggi dapat menyesuaikan data secara berlebihan, sehingga menangkap noise, bukan sinyal.<\/li>\n<li><strong>Penafsiran<\/strong>: Interpretasi bisa lebih menantang dibandingkan dengan regresi linier sederhana.<\/li>\n<\/ul>\n<h2>Jenis Regresi Polinomial<\/h2>\n<p>Regresi polinomial dapat dikategorikan berdasarkan derajat polinomialnya:<\/p>\n<table>\n<thead>\n<tr>\n<th>Derajat<\/th>\n<th>Keterangan<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>1<\/td>\n<td>Linier (Garis Lurus)<\/td>\n<\/tr>\n<tr>\n<td>2<\/td>\n<td>Kuadrat (Kurva Parabola)<\/td>\n<\/tr>\n<tr>\n<td>3<\/td>\n<td>Kubik (Kurva Berbentuk S)<\/td>\n<\/tr>\n<tr>\n<td>N<\/td>\n<td>Kurva Polinomial derajat ke-n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Cara Penggunaan Regresi Polinomial, Permasalahan, dan Solusinya Terkait Penggunaannya<\/h2>\n<h3>Kegunaan:<\/h3>\n<ul>\n<li>Ekonomi dan keuangan untuk memodelkan tren nonlinier.<\/li>\n<li>Ilmu lingkungan untuk memodelkan pola pertumbuhan.<\/li>\n<li>Rekayasa untuk analisis sistem.<\/li>\n<\/ul>\n<h3>Masalah dan Solusi:<\/h3>\n<ul>\n<li><strong>Keterlaluan<\/strong>: Solusinya adalah dengan menggunakan validasi silang dan regularisasi.<\/li>\n<li><strong>Multikolinearitas<\/strong>: Solusinya adalah dengan menggunakan penskalaan atau transformasi.<\/li>\n<\/ul>\n<h2>Ciri-ciri Utama dan Perbandingan Lain dengan Istilah Serupa<\/h2>\n<table>\n<thead>\n<tr>\n<th>Fitur<\/th>\n<th>Regresi Polinomial<\/th>\n<th>Regresi linier<\/th>\n<th>Regresi Nonlinier<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Hubungan<\/td>\n<td>Nonlinier<\/td>\n<td>Linier<\/td>\n<td>Nonlinier<\/td>\n<\/tr>\n<tr>\n<td>Fleksibilitas<\/td>\n<td>Tinggi<\/td>\n<td>Rendah<\/td>\n<td>Variabel<\/td>\n<\/tr>\n<tr>\n<td>Kompleksitas Komputasi<\/td>\n<td>Sedang<\/td>\n<td>Rendah<\/td>\n<td>Tinggi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perspektif dan Teknologi Masa Depan Terkait Regresi Polinomial<\/h2>\n<p>Kemajuan dalam pembelajaran mesin dan kecerdasan buatan kemungkinan akan meningkatkan penerapan regresi polinomial, menggabungkan teknik seperti regularisasi, metode ansambel, dan penyetelan hyperparameter otomatis.<\/p>\n<h2>Bagaimana Server Proxy Dapat Digunakan atau Diasosiasikan dengan Regresi Polinomial<\/h2>\n<p>Server proxy, seperti yang disediakan oleh OneProxy, dapat digunakan bersama dengan regresi polinomial dalam pengumpulan dan analisis data. Dengan mengizinkan akses data yang aman dan anonim, server proxy dapat memfasilitasi pengumpulan informasi untuk pemodelan, memastikan hasil yang tidak memihak, dan kepatuhan terhadap peraturan privasi.<\/p>\n<h2>tautan yang berhubungan<\/h2>\n<ul>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/Polynomial_regression\" target=\"_new\" rel=\"noopener nofollow\">Wikipedia: Regresi Polinomial<\/a><\/li>\n<li><a href=\"https:\/\/www.jstor.org\/stable\/xyz\" target=\"_new\" rel=\"noopener nofollow\">Metode Pembelajaran Statistik Regresi Polinomial<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/id\/\" target=\"_new\" rel=\"noopener\">OneProxy: Pengumpulan Data Aman untuk Analisis<\/a><\/li>\n<\/ul>","protected":false},"featured_media":469187,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478465","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Polynomial Regression<\/mark>","faq_items":[{"question":"What is Polynomial Regression?","answer":"<p>Polynomial Regression is a statistical technique that models the relationship between an independent variable <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>X<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">X<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.07847em;\">X<\/span><\/span><\/span><\/span><\/span> and a dependent variable <span class=\"math math-inline\"><span class=\"katex\"><span class=\"katex-mathml\"><math ><semantics><mrow><mi>y<\/mi><\/mrow><annotation encoding=\"application\/x-tex\">y<\/annotation><\/semantics><\/math><\/span><span class=\"katex-html\" aria-hidden=\"true\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.625em; vertical-align: -0.1944em;\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.03588em;\">y<\/span><\/span><\/span><\/span><\/span> as an nth degree polynomial. Unlike linear regression, it fits a curve to the data points, allowing for the modeling of nonlinear relationships.<\/p>"},{"question":"What is the history of Polynomial Regression?","answer":"<p>Polynomial Regression has its roots in polynomial interpolation, which dates back to the mathematical works of Isaac Newton and Carl Friedrich Gauss. It started to gain traction in the 20th century with advancements in computational tools.<\/p>"},{"question":"How does Polynomial Regression work?","answer":"<p>Polynomial Regression works by finding the coefficients that minimize the sum of the squared differences between the observed values and the values predicted by the polynomial model. This is done through the method of least squares, and the process includes choosing the degree of the polynomial, transforming the data, fitting the model, and evaluating its fit.<\/p>"},{"question":"What are the key features of Polynomial Regression?","answer":"<p>Key features of Polynomial Regression include its flexibility in modeling nonlinear relationships, its extension of linear regression techniques, a potential risk of overfitting with higher-degree polynomials, and the challenge of interpretation compared to simpler models.<\/p>"},{"question":"What types of Polynomial Regression exist?","answer":"<p>Polynomial Regression can be categorized based on the degree of the polynomial, with common examples being linear (1st degree), quadratic (2nd degree), cubic (3rd degree), and nth degree polynomial curves.<\/p>"},{"question":"How can Polynomial Regression be used, and what problems may arise?","answer":"<p>Polynomial Regression is used in various fields like economics, environmental sciences, and engineering. Common problems include overfitting, which can be addressed by using cross-validation and regularization, and multicollinearity, which can be resolved through scaling or transformation.<\/p>"},{"question":"How does Polynomial Regression compare to Linear and Nonlinear Regression?","answer":"<p>Polynomial Regression is nonlinear and offers high flexibility, unlike linear regression. It has moderate computational complexity compared to the low complexity of linear regression and the potentially high complexity of other nonlinear regression methods.<\/p>"},{"question":"What are the future perspectives and technologies related to Polynomial Regression?","answer":"<p>Future advancements in machine learning and artificial intelligence are likely to enhance Polynomial Regression, with techniques like regularization, ensemble methods, and automated hyperparameter tuning becoming more prevalent.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with Polynomial Regression?","answer":"<p>Proxy servers, such as those provided by OneProxy, can be used with Polynomial Regression in data gathering and analysis. They allow secure and anonymous access to data, facilitating the collection of information for modeling and ensuring unbiased results while adhering to privacy regulations.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/id\/wp-json\/wp\/v2\/wiki\/478465","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/id\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/id\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/id\/wp-json\/wp\/v2\/wiki\/478465\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/id\/wp-json\/wp\/v2\/media\/469187"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/id\/wp-json\/wp\/v2\/media?parent=478465"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}