Implementing boosting techniques in machine learning using sklearn

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Data Science Webinars

Implementing boosting techniques in machine learning using sklearn

August 28, 2019|Darshak Shah

Darshak has always been an analytic thinker, whether it’s coming up with the best way to play solitaire or creating a plan for product testing. He likes to break problems into their most basic components and then analyze them to find a solution. He started his career with IBM – Linux Technology Center, and after working there for 6 years, went to Boston for an MBA at Babson College. It is there that he discovered his true passion for analytics, as he worked for Grand Circle Corporation in Boston as their VP of Analytics. He then moved to India to join EarlySalary, while it was still at a very early stage and built their Decision Engine. After a stint at Searce as Head of AI & ML practice, he has decided to get into entrepreneurship and start his own business. He believes in lifelong learning and always being curious.

Key takeaways for you

  • Intuition behind Boosting
  • Working of Boosting Models
  • Understanding of different types of Boosting
  • Implementation of Boosting using Sklearn and XGBoost

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