Balancing Speed and Accuracy in Model Development

Balancing Speed and Accuracy in Model Development

Explore the critical balance between speed and accuracy in model development through this insightful 25-minute conference talk by Ivan Popov at Conf42 Python 2024. Delve into factors impacting model performance, business implications, and real-world examples. Learn optimization strategies, including data quality assessment, preprocessing techniques, and feature selection using SHAP values. Discover how to identify inefficiencies with tools like Yappi and compare popular models such as XGBoost and LightGBM. Gain valuable insights on aligning model development with business objectives and making informed decisions in the machine learning process.