Localized Explainability for Machine Learning Valuation Models
2024
- 1Usage
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Artifact Description
Machine Learning (ML) models have demonstrated remarkable performance in the valuation of real property but are often perceived as black boxes, raising concerns about trust and transparency. Explainability is the concept that clarifies the output of an ML model in a way that “makes sense” to people. At MPAC, in Ontario, Canada, we implement global and local approaches to explain our machine learning model behaviours. In the global approach, we show a big-picture view of the model, and how the features collectively affect the results. In the local approach, we concentrate on individual predictions by generating instance-specific explanations. SHapley Additive exPlanations (SHAP) is a unified framework that can be used for explaining the prediction of our ML model. Typically, the baseline value for these explanations is the average of predicted values and it is used for all properties within the model. We have customized the baseline for each property according to the typical property in a neighbourhood, which increases the relevance to the subject property and the explainability of the model.
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