The Feature Importances plot helps us understand which features are most influential in the model’s predictions, and provides us insights into the underlying patterns in the data. It increases the interpretability of the model by showing us how much each feature contributes to the prediction. It can also be used to identify and possibly remove features that have little to no importance, simplifying the model and reducing the risk of overfitting. Features with higher importance should be carefully considered in the context of the dataset, as they are driving the model’s decisions. Conversely, features with very low importance might be candidates for removal in future model iterations. The features are ordered from least to most important, providing a clear visual representation of their significance in the model. Positive values indicate a positive relationship with the target variable. Negative values indicate a negative relationship with the target variable. Higher magnitudes indicate more important features. Features at the top of the plot are the most important, regardless of the sign of their coefficients.