Fig. 1
From: Examining characteristics of predictive models with imbalanced big data
(Feature Importance (FI) scores): This figure shows the FI scores (scaled between 0 and 1) generated for both original datasets. Horizontal dashed lines on the figure represent the cut-offs for Feature Selection. The figure also shows the proportions of nominal, continuous, and one-hot encoded features. a shows that the feature with the highest score has a continuous value. The features start to score below 0.04 and 0.004 after the top 60 and 120 features, respectively. b The feature with the highest score has a one-hot encoded value. Also, it is noticeable that all the features with continuous values (total of 8) are included in the top 20 selected features