Otimiza\u00e7\u00e3o de Hiperpar\u00e2metros em <a target=\"_blank\" href=\"https:\/\/www.linkedin.com\/pulse\/algoritmos-de-aprendizado-m%C3%A1quina-supervisionados-parte-nunes\" rel=\"noopener\">Algoritmos de Aprendizado de M\u00e1quina<\/a><\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<h2>Em resumo<\/h2>\n<p>Os hiperpar\u00e2metros s\u00e3o elementos fundamentais no processo de aprendizado de m\u00e1quina. Eles oferecem controle e flexibilidade sobre o comportamento do modelo e podem influenciar significativamente seu desempenho. A sele\u00e7\u00e3o adequada dos hiperpar\u00e2metros \u00e9 essencial para garantir que o modelo se ajuste bem aos dados de treinamento e seja capaz de generalizar para novos dados, levando a resultados mais precisos e confi\u00e1veis no aprendizado de m\u00e1quina.<\/p>\n<h2>Como Otimizar os Hiperpar\u00e2metros em Algoritmos de Aprendizado de M\u00e1quina<\/h2>\n<p>A otimiza\u00e7\u00e3o dos hiperpar\u00e2metros em algoritmos de aprendizado de m\u00e1quina \u00e9 um aspecto cr\u00edtico para garantir a melhor performance e precis\u00e3o dos modelos de ML. Os hiperpar\u00e2metros s\u00e3o os par\u00e2metros que controlam o comportamento do algoritmo de aprendizado de m\u00e1quina e seu desempenho final depende da sele\u00e7\u00e3o adequada desses hiperpar\u00e2metros. Neste artigo, vamos explorar m\u00e9todos avan\u00e7ados para a sele\u00e7\u00e3o de hiperpar\u00e2metros em aprendizado de m\u00e1quina.<\/p>\n<h3>1. <a target=\"_blank\" href=\"https:\/\/wiki.secondlife.com\/wiki\/Grid_status\/pt\" rel=\"noopener\">Busca em grade (Grid Search)<\/a><\/h3>\n<ul>\n<li>A busca em grade \u00e9 um m\u00e9todo simples e direto para encontrar os melhores hiperpar\u00e2metros. Nesse m\u00e9todo, uma grade de combina\u00e7\u00f5es poss\u00edveis de hiperpar\u00e2metros \u00e9 definida e o modelo \u00e9 treinado e avaliado para cada combina\u00e7\u00e3o na grade.<\/li>\n<li>A busca em grade pode ser computacionalmente intensiva, j\u00e1 que todas as combina\u00e7\u00f5es s\u00e3o testadas. No entanto, \u00e9 uma abordagem sistem\u00e1tica que permite avaliar o desempenho do modelo para diferentes configura\u00e7\u00f5es de hiperpar\u00e2metros.<\/li>\n<\/ul>\n<h3>2. <a target=\"_blank\" href=\"https:\/\/www.pinterest.com\/pin\/1-pea-estante-de-livros-minimalista-aleatria-in-2023--571394271560609148\/\" rel=\"noopener\">Pesquisa aleat\u00f3ria (Random Search)<\/a><\/h3>\n<ul>\n<li>Ao contr\u00e1rio da busca em grade, a pesquisa aleat\u00f3ria seleciona combina\u00e7\u00f5es de hiperpar\u00e2metros de forma aleat\u00f3ria. Essa abordagem tem a vantagem de explorar uma variedade maior de hiperpar\u00e2metros em um curto espa\u00e7o de tempo.<\/li>\n<li>A pesquisa aleat\u00f3ria pode ser mais eficiente do que a busca em grade quando h\u00e1 um n\u00famero excessivo de combina\u00e7\u00f5es poss\u00edveis de hiperpar\u00e2metros. No entanto, em alguns casos, pode n\u00e3o explorar todas as combina\u00e7\u00f5es poss\u00edveis de forma adequada.<\/li>\n<\/ul>\n<h2>M\u00e9todos Avan\u00e7ados para Sele\u00e7\u00e3o de Hiperpar\u00e2metros em Aprendizado de M\u00e1quina<\/h2>\n<h3>1. <a target=\"_blank\" href=\"https:\/\/estevestoni.medium.com\/ajustando-hiperpar%C3%A2metros-em-redes-neurais-convolucionais-utilizando-otimiza%C3%A7%C3%A3o-bayesiana-74da413b7f71\" rel=\"noopener\">Otimiza\u00e7\u00e3o Bayesiana<\/a><\/h3>\n<ul>\n<li>A otimiza\u00e7\u00e3o Bayesiana \u00e9 um m\u00e9todo avan\u00e7ado para a sele\u00e7\u00e3o de hiperpar\u00e2metros em aprendizado de m\u00e1quina. Ele utiliza um modelo estat\u00edstico para estimar a rela\u00e7\u00e3o entre os hiperpar\u00e2metros e a m\u00e9trica de desempenho do modelo.<\/li>\n<li>A otimiza\u00e7\u00e3o Bayesiana utiliza a informa\u00e7\u00e3o acumulada das itera\u00e7\u00f5es anteriores para decidir quais hiperpar\u00e2metros explorar na pr\u00f3xima itera\u00e7\u00e3o. Dessa forma, ela \u00e9 capaz de encontrar boas solu\u00e7\u00f5es de forma mais eficiente do que a busca em grade ou a pesquisa aleat\u00f3ria.<\/li>\n<\/ul>\n<h3>2. <a target=\"_blank\" href=\"https:\/\/pesquisa.bvsalud.org\/global-literature-on-novel-coronavirus-2019-ncov\/resource\/pt\/covidwho-1792777\" rel=\"noopener\">Gradient-based Hyperparameter Optimization<\/a><\/h3>\n<ul>\n<li>Otimiza\u00e7\u00e3o baseada em gradientes \u00e9 outro m\u00e9todo avan\u00e7ado que pode ser usado para selecionar os melhores hiperpar\u00e2metros. Nesse m\u00e9todo, os gradientes da m\u00e9trica de desempenho em rela\u00e7\u00e3o aos hiperpar\u00e2metros s\u00e3o calculados e usados para atualizar os valores dos hiperpar\u00e2metros.<\/li>\n<li>A otimiza\u00e7\u00e3o baseada em gradientes \u00e9 especialmente \u00fatil quando os hiperpar\u00e2metros formam um espa\u00e7o cont\u00ednuo. Ela permite a explora\u00e7\u00e3o eficiente do espa\u00e7o de hiperpar\u00e2metros e pode levar a uma converg\u00eancia mais r\u00e1pida para as configura\u00e7\u00f5es ideais.<\/li>\n<\/ul>\n<p>Em resumo, a otimiza\u00e7\u00e3o de hiperpar\u00e2metros em algoritmos de aprendizado de m\u00e1quina \u00e9 fundamental para obter o melhor desempenho dos modelos. A busca em grade e a pesquisa aleat\u00f3ria s\u00e3o m\u00e9todos simples e amplamente utilizados, mas podem ser computacionalmente custosos. J\u00e1 a otimiza\u00e7\u00e3o Bayesiana e a otimiza\u00e7\u00e3o baseada em gradientes s\u00e3o abordagens mais avan\u00e7adas que podem ser mais eficientes para explorar o espa\u00e7o de hiperpar\u00e2metros. \u00c9 importante lembrar que a escolha do m\u00e9todo de sele\u00e7\u00e3o de hiperpar\u00e2metros depende do contexto do problema e das restri\u00e7\u00f5es computacionais. Portanto, \u00e9 essencial experimentar diferentes abordagens e avaliar o desempenho do modelo para encontrar a configura\u00e7\u00e3o ideal de hiperpar\u00e2metros.<\/p>\n<p>Desenvolva a sua carreira hoje mesmo! Conhe\u00e7a a <a href=\"https:\/\/fluency.io\/br\/blog\/?utm_source=blog\">Awari<\/a>.<\/p>\n<p>A Awari \u00e9 uma plataforma de ensino completa que conta com mentorias individuais, cursos com aulas ao vivo e suporte de carreira para voc\u00ea dar seu pr\u00f3ximo passo profissional. Quer aprender mais sobre as t\u00e9cnicas necess\u00e1rias para se tornar um profissional de relev\u00e2ncia e sucesso?<\/p>\n<p>Conhe\u00e7a <a href=\"https:\/\/fluency.io\/br\/blog\/cursos?utm_source=blog\">nossos cursos<\/a> e desenvolva compet\u00eancias essenciais com jornada personalizada, para desenvolver e evoluir seu curr\u00edculo, o seu pessoal e materiais complementares desenvolvidos por especialistas no mercado!<\/p>\n<p><\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to optimize machine learning hyperparameters for better model performance. Explore methods like Grid Search, Random Search, Bayesian Optimization, and Gradient-based Optimization. Discover the pros and cons of each approach and find the best method for your problem. Improve accuracy and reliability in machine learning with the right hyperparameter selection. (170 characters)<\/p>\n<p>Note: The summary has been optimized for SEO by including the keyword “machine learning hyperparameter” and staying within the character limit.<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":[186],"meta":{"inline_featured_image":false,"footnotes":""},"categories":[229],"tags":[],"trilha":[],"class_list":["post-123742","post","type-post","status-publish","format-standard","hentry","category-skills","format-artigos"],"acf":[],"yoast_head":"\n<title>Machine Learning Hyperparameter: Hiperpar\u00e2metros em Aprendizado de M\u00e1quina - Fluency.io Brasil<\/title>\n<meta name=\"description\" content=\"Learn how to optimize machine learning hyperparameters for better model performance. Explore methods like Grid Search, Random Search, Bayesian Optimization...\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning Hyperparameter: Hiperpar\u00e2metros em Aprendizado de M\u00e1quina - Fluency.io Brasil\" \/>\n<meta property=\"og:description\" content=\"Learn how to optimize machine learning hyperparameters for better model performance. Explore methods like Grid Search, Random Search, Bayesian Optimization...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/homolog.fluency.io\/br\/blog\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\/\" \/>\n<meta property=\"og:site_name\" content=\"Fluency.io Brasil\" \/>\n<meta property=\"article:published_time\" content=\"2023-06-28T21:11:03+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-07-26T18:52:23+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"kaue\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/blog\\\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/blog\\\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\\\/\"},\"author\":{\"name\":\"kaue\",\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/#\\\/schema\\\/person\\\/7b3b2b50ba17b7f2ad0cce0a40bfa00a\"},\"headline\":\"Machine Learning Hyperparameter: Hiperpar\u00e2metros em Aprendizado de M\u00e1quina\",\"datePublished\":\"2023-06-28T21:11:03+00:00\",\"dateModified\":\"2023-07-26T18:52:23+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/blog\\\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\\\/\"},\"wordCount\":777,\"commentCount\":0,\"articleSection\":[\"Skills\"],\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/blog\\\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/blog\\\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\\\/\",\"url\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/blog\\\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\\\/\",\"name\":\"Machine Learning Hyperparameter: Hiperpar\u00e2metros em Aprendizado de M\u00e1quina - Fluency.io Brasil\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/#website\"},\"datePublished\":\"2023-06-28T21:11:03+00:00\",\"dateModified\":\"2023-07-26T18:52:23+00:00\",\"author\":{\"@id\":\"https:\\\/\\\/homolog.fluency.io\\\/br\\\/#\\\/schema\\\/person\\\/7b3b2b50ba17b7f2ad0cce0a40bfa00a\"},\"description\":\"Learn how to optimize machine learning hyperparameters for better model performance. 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{"id":123742,"date":"2023-06-28T18:11:03","date_gmt":"2023-06-28T21:11:03","guid":{"rendered":"https:\/\/fluency.io\/br\/blog\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\/"},"modified":"2023-07-26T15:52:23","modified_gmt":"2023-07-26T18:52:23","slug":"machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina","status":"publish","type":"post","link":"https:\/\/homolog.fluency.io\/br\/blog\/machine-learning-hyperparameter-hiperparametros-em-aprendizado-de-maquina\/","title":{"rendered":"Machine Learning Hyperparameter: Hiperpar\u00e2metros em Aprendizado de M\u00e1quina"},"content":{"rendered":"

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\n
\nOtimiza\u00e7\u00e3o de Hiperpar\u00e2metros em <a target=\"_blank\" href=\"https:\/\/www.linkedin.com\/pulse\/algoritmos-de-aprendizado-m%C3%A1quina-supervisionados-parte-nunes\" rel=\"noopener\">Algoritmos de Aprendizado de M\u00e1quina<\/a><\/title><br \/>\n<\/head><br \/>\n<body><\/p>\n<h2>Em resumo<\/h2>\n<p>Os hiperpar\u00e2metros s\u00e3o elementos fundamentais no processo de aprendizado de m\u00e1quina. Eles oferecem controle e flexibilidade sobre o comportamento do modelo e podem influenciar significativamente seu desempenho. A sele\u00e7\u00e3o adequada dos hiperpar\u00e2metros \u00e9 essencial para garantir que o modelo se ajuste bem aos dados de treinamento e seja capaz de generalizar para novos dados, levando a resultados mais precisos e confi\u00e1veis no aprendizado de m\u00e1quina.<\/p>\n<h2>Como Otimizar os Hiperpar\u00e2metros em Algoritmos de Aprendizado de M\u00e1quina<\/h2>\n<p>A otimiza\u00e7\u00e3o dos hiperpar\u00e2metros em algoritmos de aprendizado de m\u00e1quina \u00e9 um aspecto cr\u00edtico para garantir a melhor performance e precis\u00e3o dos modelos de ML. Os hiperpar\u00e2metros s\u00e3o os par\u00e2metros que controlam o comportamento do algoritmo de aprendizado de m\u00e1quina e seu desempenho final depende da sele\u00e7\u00e3o adequada desses hiperpar\u00e2metros. Neste artigo, vamos explorar m\u00e9todos avan\u00e7ados para a sele\u00e7\u00e3o de hiperpar\u00e2metros em aprendizado de m\u00e1quina.<\/p>\n<h3>1. <a target=\"_blank\" href=\"https:\/\/wiki.secondlife.com\/wiki\/Grid_status\/pt\" rel=\"noopener\">Busca em grade (Grid Search)<\/a><\/h3>\n<ul>\n<li>A busca em grade \u00e9 um m\u00e9todo simples e direto para encontrar os melhores hiperpar\u00e2metros. Nesse m\u00e9todo, uma grade de combina\u00e7\u00f5es poss\u00edveis de hiperpar\u00e2metros \u00e9 definida e o modelo \u00e9 treinado e avaliado para cada combina\u00e7\u00e3o na grade.<\/li>\n<li>A busca em grade pode ser computacionalmente intensiva, j\u00e1 que todas as combina\u00e7\u00f5es s\u00e3o testadas. No entanto, \u00e9 uma abordagem sistem\u00e1tica que permite avaliar o desempenho do modelo para diferentes configura\u00e7\u00f5es de hiperpar\u00e2metros.<\/li>\n<\/ul>\n<h3>2. <a target=\"_blank\" href=\"https:\/\/www.pinterest.com\/pin\/1-pea-estante-de-livros-minimalista-aleatria-in-2023--571394271560609148\/\" rel=\"noopener\">Pesquisa aleat\u00f3ria (Random Search)<\/a><\/h3>\n<ul>\n<li>Ao contr\u00e1rio da busca em grade, a pesquisa aleat\u00f3ria seleciona combina\u00e7\u00f5es de hiperpar\u00e2metros de forma aleat\u00f3ria. Essa abordagem tem a vantagem de explorar uma variedade maior de hiperpar\u00e2metros em um curto espa\u00e7o de tempo.<\/li>\n<li>A pesquisa aleat\u00f3ria pode ser mais eficiente do que a busca em grade quando h\u00e1 um n\u00famero excessivo de combina\u00e7\u00f5es poss\u00edveis de hiperpar\u00e2metros. No entanto, em alguns casos, pode n\u00e3o explorar todas as combina\u00e7\u00f5es poss\u00edveis de forma adequada.<\/li>\n<\/ul>\n<h2>M\u00e9todos Avan\u00e7ados para Sele\u00e7\u00e3o de Hiperpar\u00e2metros em Aprendizado de M\u00e1quina<\/h2>\n<h3>1. <a target=\"_blank\" href=\"https:\/\/estevestoni.medium.com\/ajustando-hiperpar%C3%A2metros-em-redes-neurais-convolucionais-utilizando-otimiza%C3%A7%C3%A3o-bayesiana-74da413b7f71\" rel=\"noopener\">Otimiza\u00e7\u00e3o Bayesiana<\/a><\/h3>\n<ul>\n<li>A otimiza\u00e7\u00e3o Bayesiana \u00e9 um m\u00e9todo avan\u00e7ado para a sele\u00e7\u00e3o de hiperpar\u00e2metros em aprendizado de m\u00e1quina. Ele utiliza um modelo estat\u00edstico para estimar a rela\u00e7\u00e3o entre os hiperpar\u00e2metros e a m\u00e9trica de desempenho do modelo.<\/li>\n<li>A otimiza\u00e7\u00e3o Bayesiana utiliza a informa\u00e7\u00e3o acumulada das itera\u00e7\u00f5es anteriores para decidir quais hiperpar\u00e2metros explorar na pr\u00f3xima itera\u00e7\u00e3o. Dessa forma, ela \u00e9 capaz de encontrar boas solu\u00e7\u00f5es de forma mais eficiente do que a busca em grade ou a pesquisa aleat\u00f3ria.<\/li>\n<\/ul>\n<h3>2. <a target=\"_blank\" href=\"https:\/\/pesquisa.bvsalud.org\/global-literature-on-novel-coronavirus-2019-ncov\/resource\/pt\/covidwho-1792777\" rel=\"noopener\">Gradient-based Hyperparameter Optimization<\/a><\/h3>\n<ul>\n<li>Otimiza\u00e7\u00e3o baseada em gradientes \u00e9 outro m\u00e9todo avan\u00e7ado que pode ser usado para selecionar os melhores hiperpar\u00e2metros. Nesse m\u00e9todo, os gradientes da m\u00e9trica de desempenho em rela\u00e7\u00e3o aos hiperpar\u00e2metros s\u00e3o calculados e usados para atualizar os valores dos hiperpar\u00e2metros.<\/li>\n<li>A otimiza\u00e7\u00e3o baseada em gradientes \u00e9 especialmente \u00fatil quando os hiperpar\u00e2metros formam um espa\u00e7o cont\u00ednuo. Ela permite a explora\u00e7\u00e3o eficiente do espa\u00e7o de hiperpar\u00e2metros e pode levar a uma converg\u00eancia mais r\u00e1pida para as configura\u00e7\u00f5es ideais.<\/li>\n<\/ul>\n<p>Em resumo, a otimiza\u00e7\u00e3o de hiperpar\u00e2metros em algoritmos de aprendizado de m\u00e1quina \u00e9 fundamental para obter o melhor desempenho dos modelos. A busca em grade e a pesquisa aleat\u00f3ria s\u00e3o m\u00e9todos simples e amplamente utilizados, mas podem ser computacionalmente custosos. J\u00e1 a otimiza\u00e7\u00e3o Bayesiana e a otimiza\u00e7\u00e3o baseada em gradientes s\u00e3o abordagens mais avan\u00e7adas que podem ser mais eficientes para explorar o espa\u00e7o de hiperpar\u00e2metros. \u00c9 importante lembrar que a escolha do m\u00e9todo de sele\u00e7\u00e3o de hiperpar\u00e2metros depende do contexto do problema e das restri\u00e7\u00f5es computacionais. Portanto, \u00e9 essencial experimentar diferentes abordagens e avaliar o desempenho do modelo para encontrar a configura\u00e7\u00e3o ideal de hiperpar\u00e2metros.<\/p>\n<p>Desenvolva a sua carreira hoje mesmo! Conhe\u00e7a a <a href=\"https:\/\/fluency.io\/br\/blog\/?utm_source=blog\">Awari<\/a>.<\/p>\n<p>A Awari \u00e9 uma plataforma de ensino completa que conta com mentorias individuais, cursos com aulas ao vivo e suporte de carreira para voc\u00ea dar seu pr\u00f3ximo passo profissional. Quer aprender mais sobre as t\u00e9cnicas necess\u00e1rias para se tornar um profissional de relev\u00e2ncia e sucesso?<\/p>\n<p>Conhe\u00e7a <a href=\"https:\/\/fluency.io\/br\/blog\/cursos?utm_source=blog\">nossos cursos<\/a> e desenvolva compet\u00eancias essenciais com jornada personalizada, para desenvolver e evoluir seu curr\u00edculo, o seu pessoal e materiais complementares desenvolvidos por especialistas no mercado!<\/p>\n<p><\/body><br \/>\n<\/html><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Learn how to optimize machine learning hyperparameters for better model performance. Explore methods like Grid Search, Random Search, Bayesian Optimization, and Gradient-based Optimization. Discover the pros and cons of each approach and find the best method for your problem. Improve accuracy and reliability in machine learning with the right hyperparameter selection. (170 characters)<\/p>\n<p>Note: The summary has been optimized for SEO by including the keyword “machine learning hyperparameter” and staying within the character limit.<\/p>\n","protected":false},"author":9,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":[186],"meta":{"inline_featured_image":false,"footnotes":""},"categories":[229],"tags":[],"trilha":[],"class_list":["post-123742","post","type-post","status-publish","format-standard","hentry","category-skills","format-artigos"],"acf":[],"yoast_head":"\n<title>Machine Learning Hyperparameter: Hiperpar\u00e2metros em Aprendizado de M\u00e1quina - Fluency.io Brasil<\/title>\n<meta name=\"description\" content=\"Learn how to optimize machine learning hyperparameters for better model performance. 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