Welcome to Data Awesome #13! Superstition has no place for data folks. π Letβs get to it! π Awesome Articles π One-hot encoding is the first encoding method for nominal categorical data that most people trained in statistics learn. When using a linear model, you were probably taught that you must drop one of the resulting columns. Damien Martin makes a bunch of good points about when you donβt want to drop one of the columns in a machine learning context. He also provides helpful guidelines in
Data Awesome #13
Data Awesome #13
Data Awesome #13
Welcome to Data Awesome #13! Superstition has no place for data folks. π Letβs get to it! π Awesome Articles π One-hot encoding is the first encoding method for nominal categorical data that most people trained in statistics learn. When using a linear model, you were probably taught that you must drop one of the resulting columns. Damien Martin makes a bunch of good points about when you donβt want to drop one of the columns in a machine learning context. He also provides helpful guidelines in