{"id":1162,"date":"2019-04-17T17:05:52","date_gmt":"2019-04-17T17:05:52","guid":{"rendered":"http:\/\/127.0.0.1:8086\/?p=1162"},"modified":"2021-03-16T15:52:23","modified_gmt":"2021-03-16T15:52:23","slug":"scrapping","status":"publish","type":"post","link":"https:\/\/deathbycaptcha.com\/blog\/feeds\/scrapping","title":{"rendered":"Web Scrapping"},"content":{"rendered":"<div class=\"feedzy-a20c472a85144ecc7bf6aeaaafa1fe3d feedzy-rss\"><div class=\"rss_header\"><h2><a href=\"\" class=\"rss_title\" rel=\"noopener\"><\/a> <span class=\"rss_description\"> <\/span><\/h2><\/div><ul><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><span class=\"title\"><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2026\/07\/class-imbalance-ml\/\" target=\"_blank\" rel=\" noopener\">Handling Imbalanced Classification: What Works Better Than SMOTE<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/www.analyticsvidhya.com\" target=\"_blank\" title=\"www.analyticsvidhya.com\">Vipin Vashisth<\/a> on July 12, 2026 at 9:57 am <\/small><p>Most real-world classification problems are imbalanced. Fraud, disease, churn, and defects are rare by nature. Standard classifiers chase accuracy, so they quietly ignore the very class you care about. For years, SMOTE was the reflex fix that everyone reached for first. But SMOTE often fails on the messy, high-dimensional data that production systems actually see. \nThe post Handling Imbalanced Classification: What Works Better Than SMOTE appeared first on Analytics Vidhya.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><span class=\"title\"><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2026\/07\/rag-evaluation-frameworks\/\" target=\"_blank\" rel=\" noopener\">RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/www.analyticsvidhya.com\" target=\"_blank\" title=\"www.analyticsvidhya.com\">Soumil Jain<\/a> on July 11, 2026 at 6:16 pm <\/small><p>LLMs are getting stronger every day, and building a RAG pipeline has never been easier. Knowing whether it actually works is not. Most teams ship a RAG system, see decent-looking answers, and call it done, until users hit hallucination, missing context, or irrelevant chunks. That\u2019s where evaluation frameworks come in. RAGAS, TruLens, and DeepEval are \nThe post RAG Evaluation Frameworks Compared: RAGAS vs TruLens vs DeepEval appeared first on Analytics Vidhya.<\/p><\/div><\/li><li  style=\"padding: 15px 0 25px\" class=\"rss_item\"><div class=\"rss_image\" style=\"height:150px;width:150px;\"><a href=\"https:\/\/www.kdnuggets.com\/fine-tuning-explained-for-noobs-how-pretrained-models-learn-new-skills\" target=\"_blank\" rel=\" noopener\" title=\"Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills)\" style=\"height:150px;width:150px;\"><img decoding=\"async\" src=\"https:\/\/www.kdnuggets.com\/wp-content\/uploads\/Noob-Series-Fine-Tuning-Explained.png\" title=\"Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills)\" style=\"height:150px;width:150px;\"><\/a><\/div><span class=\"title\"><a href=\"https:\/\/www.kdnuggets.com\/fine-tuning-explained-for-noobs-how-pretrained-models-learn-new-skills\" target=\"_blank\" rel=\" noopener\">Fine-Tuning Explained for Noobs (How Pretrained Models Learn New Skills)<\/a><\/span><div class=\"rss_content\" style=\"\"><small>by <a href=\"\/\/www.kdnuggets.com\" target=\"_blank\" title=\"www.kdnuggets.com\">Kanwal Mehreen<\/a> on July 10, 2026 at 2:00 pm <\/small><p>You don't need a PhD to understand fine-tuning. This article explains how pretrained models learn new skills through fine-tuning.<\/p><\/div><\/li><\/ul> <\/div><style type=\"text\/css\" media=\"all\">.feedzy-rss .rss_item .rss_image{float:left;position:relative;border:none;text-decoration:none;max-width:100%}.feedzy-rss .rss_item .rss_image span{display:inline-block;position:absolute;width:100%;height:100%;background-position:50%;background-size:cover}.feedzy-rss .rss_item .rss_image{margin:.3em 1em 0 0;content-visibility:auto}.feedzy-rss ul{list-style:none}.feedzy-rss ul li{display:inline-block}<\/style>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[58],"tags":[],"class_list":["post-1162","post","type-post","status-publish","format-standard","hentry","category-feeds"],"_links":{"self":[{"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/posts\/1162","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/comments?post=1162"}],"version-history":[{"count":5,"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/posts\/1162\/revisions"}],"predecessor-version":[{"id":2109,"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/posts\/1162\/revisions\/2109"}],"wp:attachment":[{"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/media?parent=1162"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/categories?post=1162"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/deathbycaptcha.com\/blog\/wp-json\/wp\/v2\/tags?post=1162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}