Walking the Random Forest and boosting the trees
Data Science Webinar by Kevin Lemagnen, Cambridge Spark
Deep Learning is all the rage, but ensemble models are still in the game. With libraries such as the recent and performant LightGBM, the Kaggle superstar XGboost or the classic Random Forest from scikit-learn, ensembles models are a must-have in a data scientist’s toolbox. They’ve been proven to provide good performance on a wide range of problems, and are usually simpler to tune and interpret. This talk focuses on two of the most popular tree-based ensemble models. You will learn about Random Forest and Gradient Boosting, relying respectively on bagging and boosting. This talk will demonstrate how to apply these techniques on a real-world business problem in a live-coding session using the latest implementations available in the Python ecosystem.
About the speaker: Kevin Lemagnen
Kevin is a Senior Data Scientist at Cambridge Spark. He has lead development of data products for the energy sector and worked for the telecommunications industry at Qualcomm. He was also a visiting researcher at Stanford University. Kevin has delivered data science and machine learning training courses to various clients from industries that include finance, engineering and research helping individuals leverage the latest techniques.
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