Column importance

Column importance in machine learning refers to the ability to determine which features of the data are most important in predicting the target variable. It is a way to understand the relationship between the input features and the target variable, and to identify which features have the most impact on the model's predictions. We can see a sample feature importance plot below.



Column importance is important because it helps you understand which features are most important in making accurate predictions and can also help you determine which features to keep or remove from your model. This can lead to improved model performance, faster training, and simpler models that are easier to interpret and use.

Updated on: 15/02/2023