Explores the pros and cons of discretizing continuous features in machine learning, with a practical guide using scikit-learn's KBinsDiscretizer.
Announces the addition of 6 new R programming books to the Big Book of R collection, covering statistics, machine learning, and data science.
Notes from Spark+AI Summit 2020 covering application-specific talks on ML frameworks, data engineering, feature stores, and data quality from companies like Airbnb and Netflix.
A data scientist details how a flawed train-test split method introduced bias when adding image thumbnails to a content recommendation model.
A summary of a talk on achieving top 3% in a Kaggle competition, covering validation, feature engineering, and ensemble techniques.
