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.

本文介绍了一种解释性特征重要性算法 SSFI，可以识别对于单个样本预测最重要的特征，并通过随机森林分类器 / 回归器的预测路径计算低级特征重要性，从而得出特征的相对重要性排序，同时在四个不同数据集上提出了数值和可视化结果。