Interpreting random forest classification models using a feature contribution method

By Anna Palczewska, Jan Palczewski, Richard Marchese Robinson, Daniel Neagu. 2013

144 citations recorded by the source corpus.

Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is relatively easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance . For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. By analysing feature contributions for a training dataset, the most significant variables can be determined and their typical contribution towards predictions made for individual classes, i.e., class-specific feature contribution “patterns”, are discovered. These patterns represent a standard behaviour of the model and allow for an additional assessment of the model reliability for new data. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.

Subfields: Artificial Intelligence, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vaishak Belle
2. Yanhong Wu
3. Dik Lun Lee
4. Lior Rokach
5. Bracha Shapira
6. Ronni Miller
7. Xun Zhao
8. Weiwei Cui

In Signal Processing

1. Vaishak Belle
2. Yanhong Wu
3. Dik Lun Lee
4. Lior Rokach
5. Bracha Shapira
6. Ronni Miller
7. Weiwei Cui
8. Liat Antwarg

Citations in the corpus

• Forest Floor Visualizations of Random Forests (2016, methodology): “Next, the concept of feature contributions has previously been extended to classification, where the categorical majority vote labeling were replaced with numeric probability predictions [16].”
• iForest: Interpreting Random Forests via Visual Analytics (2019, background): “Both MDA and MDI can be either applied globally on an entire dataset to reveal the overall feature importance [10] or calculated on a single prediction for a more detailed examination [43, 44].”
• Explaining anomalies detected by autoencoders using Shapley Additive Explanations (2021, methodology): “The explanations are based on existing explanation algorithms, such as the instance-level feature importance method [28], partial dependence plots [29], and local rule extraction (a variation of LIME [3]).”
• Principles and Practice of Explainable Machine Learning (2020, background): “• The authors of [64] develop an approach for assessing the importance of individual features, by computing how much the model’s accuracy drops, after excluding a feature.”
• In silico modelling of permeation enhancement potency in Caco-2 monolayers based on molecular descriptors and random forest. (2015, methodology): “To assess the mechanics of the random forest-QSAR, the package rfFC [23,24], which is a diagnostic extension for random forest, was acquired from https://r-forge.”