{"id":3523,"date":"2018-03-09T10:37:14","date_gmt":"2018-03-09T10:37:14","guid":{"rendered":"http:\/\/joapen.com\/blog\/?p=3523"},"modified":"2018-03-09T10:37:14","modified_gmt":"2018-03-09T10:37:14","slug":"overfitting","status":"publish","type":"post","link":"https:\/\/joapen.com\/blog\/2018\/03\/09\/overfitting\/","title":{"rendered":"Overfitting"},"content":{"rendered":"<h1>What is overfitting?<\/h1>\n<p>when you are preparing a machine learning solution, you work basically with data sets that contains:<\/p>\n<ul>\n<li>Data: relevant and\/or important data.<\/li>\n<li>Noise: inrelevant and\/or non important data.<\/li>\n<\/ul>\n<p>With this data you want to identify a trigger, a signal that responds to your target pattern you want that your code identifies.<\/p>\n<p>So you start identifying a pattern and you work to improve it.<\/p>\n<p>Suddenly, you improve your pattern identification so much, till a point\u00a0where you will be not just using the data but your pattern is also using the noise side of the data to trigger the signal.<\/p>\n<p>This phenomenon is not desired, and it is what is called <strong>overfitting<\/strong>.<\/p>\n<p>In the picture from the left:<\/p>\n<ul>\n<li>The black line represents a healthy pattern.<\/li>\n<li>The green line represents an overfitted pattern.<\/li>\n<\/ul>\n<p><a href=\"http:\/\/joapen.com\/blog\/2018\/03\/09\/overfitting\/overfitting\/\" rel=\"attachment wp-att-3524\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-3524\" src=\"http:\/\/joapen.com\/blog\/wp-content\/uploads\/2018\/03\/Overfitting.jpg\" alt=\"\" width=\"765\" height=\"391\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2018\/03\/Overfitting.jpg 765w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2018\/03\/Overfitting-300x153.jpg 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2018\/03\/Overfitting-500x256.jpg 500w\" sizes=\"auto, (max-width: 765px) 100vw, 765px\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>What is overfitting? when you are preparing a machine learning solution, you work basically with data sets that contains: Data: relevant and\/or important data. Noise: inrelevant and\/or non important data. With this data you want to identify a trigger, a signal that responds to your target pattern you want that your code identifies. So you &#8230; <a title=\"Overfitting\" class=\"read-more\" href=\"https:\/\/joapen.com\/blog\/2018\/03\/09\/overfitting\/\" aria-label=\"Read more about Overfitting\">Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[151],"tags":[],"class_list":["post-3523","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Overfitting -<\/title>\n<meta name=\"description\" content=\"What is overfitting? when you are preparing a machine learning solution, you work basically with data sets that contains: Data: relevant and\/or important - joapen projects\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/joapen.com\/blog\/2018\/03\/09\/overfitting\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Overfitting -\" \/>\n<meta property=\"og:description\" content=\"What is overfitting? 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