If you can, answer the question below: Write your answer in the comment box.
What is regularization?<\/p>\n","protected":false},"excerpt":{"rendered":"
If you can, answer the question below: Write your answer in the comment box. What is regularization?<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[1944],"tags":[],"class_list":["post-70998","post","type-post","status-publish","format-standard","hentry","category-matrix-and-signal-processing","item-wrap"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":16619,"url":"http:\/\/bangla.sitestree.com\/?p=16619","url_meta":{"origin":70998,"position":0},"title":"Math\/Stat\/CS\/DS Topics that you need to know (with Cognitive, Psychomotor, Affective domain skills) to become a true and great Data Scientist","author":"Sayed","date":"January 7, 2020","format":false,"excerpt":"\"The core topics are cross-validation, shrinkage methods (ridge regression, the LASSO, etc.), neural networks, gradient boosting, separating hyperplanes, support vector machines, basis expansion and regularization (e.g., smoothing splines, wavelet smoothing, kernel smoothing), generalized additive models, bump hunting, multivariate adaptive regression splines (MARS), self-organizing maps, mixture model-based clustering, ensemble learning, and\u2026","rel":"","context":"In "Math and Statistics for Data Science, and Engineering"","block_context":{"text":"Math and Statistics for Data Science, and Engineering","link":"http:\/\/bangla.sitestree.com\/?cat=1908"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":71051,"url":"http:\/\/bangla.sitestree.com\/?p=71051","url_meta":{"origin":70998,"position":1},"title":"What do desire in bias and variance in the prediction. 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