Feature Selection for Machine Learning

Explore feature selection techniques for machine learning in this 36-minute conference talk from the Data Science Festival Summer School 2021. Delve into the importance of selecting the most predictive variables when building production-ready models. Learn about the three main categories of feature selection algorithms: filter, wrapper, and embedded methods, as well as hybrid approaches. Follow along as Soledad Galli, Lead Data Scientist at Train in Data, walks through popular algorithms in each category and compares their implementations in open-source Python libraries. Gain insights into optimizing model performance and efficiency by strategically choosing the most relevant features for your machine learning projects.