{"id":3661,"date":"2024-09-07T12:40:05","date_gmt":"2024-09-07T12:40:05","guid":{"rendered":"https:\/\/workhouse.sweetdishy.com\/?p=3661"},"modified":"2024-09-07T12:40:05","modified_gmt":"2024-09-07T12:40:05","slug":"how-much-data-is-needed-for-ai","status":"publish","type":"post","link":"https:\/\/workhouse.sweetdishy.com\/index.php\/2024\/09\/07\/how-much-data-is-needed-for-ai\/","title":{"rendered":"How much data is needed for AI?"},"content":{"rendered":"\n<p>The rule-of-thumb rule is that you need\u00a0<strong>at least ten times as many data points as there are features in your dataset<\/strong>. For example, if your dataset has 10 columns or features, you should have at least 100 rows. The rule-of-thumb approach ensures that enough high-quality input exists.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The rule-of-thumb rule is that you need\u00a0at least ten times as many data points as there are features in your dataset. For example, if your dataset has 10 columns or features, you should have at least 100 rows. The rule-of-thumb approach ensures that enough high-quality input exists.<\/p>\n","protected":false},"author":1,"featured_media":3391,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[469],"tags":[],"class_list":["post-3661","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data"],"jetpack_featured_media_url":"https:\/\/workhouse.sweetdishy.com\/wp-content\/uploads\/2024\/09\/data-science-1.png","_links":{"self":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts\/3661","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/comments?post=3661"}],"version-history":[{"count":1,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts\/3661\/revisions"}],"predecessor-version":[{"id":3662,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts\/3661\/revisions\/3662"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/media\/3391"}],"wp:attachment":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/media?parent=3661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/categories?post=3661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/tags?post=3661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}