  Have you ever wondered how your feature space is impacting the prediction of
a specific sample in your dataset? In this paper, we introduce Single Sample
Feature Importance (SSFI), which is an interpretable feature importance
algorithm that allows for the identification of the most important features
that contribute to the prediction of a single sample. When a dataset can be
learned by a Random Forest classifier or regressor, SSFI shows how the Random
Forest's prediction path can be utilized for low-level feature importance
calculation. SSFI results in a relative ranking of features, highlighting those
with the greatest impact on a data point's prediction. We demonstrate these
results both numerically and visually on four different datasets.
