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K-fold Cross Validation is a powerful technique used in machine learning to assess the performance of a model. It helps in reducing bias, ensuring better generalization, and providing a more reliable estimate of model performance. In this video, we will explore the key points of K-fold Cross Validation, including how it works, its advantages, and how it can improve the accuracy of your machine learning models. 👉Subscribe to our new channel: / @varunainashots Subject-wise playlist Links: -------------------------------------------------------------------------------------------------------------------------------------- ►Design and Analysis of algorithms (DAA): • Design and Analysis of algorithms (DAA) ►Software Engineering (Complete Playlist): • Software Engineering ►Database Management System: • DBMS (Database Management system) Com... ►Cloud Computing: • Cloud Computing (Complete Course) ► Theory of Computation • TOC(Theory of Computation) ►Artificial Intelligence: • Artificial Intelligence (Complete Pla... ►Computer Networks (Complete Playlist): • Computer Networks (Complete Playlist) ►Operating System: • Operating System (Complete Playlist) ►Structured Query Language (SQL): • Structured Query Language (SQL) ►Discrete Mathematics: • Discrete Mathematics ►Compiler Design: • Compiler Design (Complete Playlist) ►Number System: • Number system ►Programming in C : • C Programming ►Data Structure: • Data Structure ►Computer Architecture : • Computer Organization and Architectur... ►Graph Theory (Complete Playlist): • Graph Theory ►Digital Logic: • Digital Logic (Complete Playlist) --------------------------------------------------------------------------------------------------------------------------------------- Our social media Links: ► Subscribe to us on YouTube: / gatesmashers ►Subscribe to our new channel: / @varunainashots ► Like our page on Facebook: / gatesmashers ► Follow us on Instagram: / gate.smashers ► Follow us on Instagram: / varunainashots ► Follow us on Telegram: https://t.me/gatesmashersofficial ► Follow us on Threads: https://www.threads.net/@gate.smashers -------------------------------------------------------------------------------------------------------------------------------------- ►For Any Query, Suggestion or notes contribution: Email us at: [email protected]