Interpretation of nonlinear relationships between process variables by use of random forests

Auret, Lidia; Aldrich, Chris

doi:10.1016/j.mineng.2012.05.008

Cited by 159 publications

(89 citation statements)

References 24 publications

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“…To visualize the direction and nature of the relationship between the most important predictors and each response, and thus assess how the influence of each predictor scales across lakes, we constructed partial dependency plots (PDP) for the most important predictors from each RF model. Partial dependence is computed by predicting the response (e.g., DCM depth or thickness) from the RF over a range of values for the variable of interest, while holding all other variables in the dataset constant (Friedman ; also see Auret and Aldrich for an excellent description). In essence, a PDP represents the relationship between a single variable and the response, after accounting for the average effects or interactions of the other predictors in the model (Carlisle et al ).…”

Section: Methodsmentioning

confidence: 99%

Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification

Leach

Beisner

Carey

et al. 2017

Limnology & Oceanography

140

139

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The vertical distribution of chlorophyll in stratified lakes and reservoirs frequently exhibits a maximum peak deep in the water column, referred to as the deep chlorophyll maximum (DCM). DCMs are ecologically important hot spots of primary production and nutrient cycling, and their location can determine vertical habitat gradients for primary consumers. Consequently, the drivers of DCM structure regulate many characteristics of aquatic food webs and biogeochemistry. Previous studies have identified light and thermal stratification as important drivers of summer DCM depth, but their relative importance across a broad range of lakes is not well resolved. We analyzed profiles of chlorophyll fluorescence, temperature, and light during summer stratification from 100 lakes in the Global Lake Ecological Observatory Network (GLEON) and quantified two characteristics of DCM structure: depth and thickness. While DCMs do form in oligotrophic lakes, we found that they can also form in eutrophic to dystrophic lakes. Using a random forest algorithm, we assessed the relative importance of variables associated with light attenuation vs. thermal stratification for predicting DCM structure in lakes that spanned broad gradients of morphometry and transparency. Our analyses revealed that light attenuation was a more important predictor of DCM depth than thermal stratification and that DCMs deepen with increasing lake clarity. DCM thickness was best predicted by lake size with larger lakes having thicker DCMs. Additionally, our analysis demonstrates that the relative importance of light and thermal stratification on DCM structure is not uniform across a diversity of lake types.

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Section: Methodsmentioning

confidence: 99%

Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification

Leach

Beisner

Carey

et al. 2017

Limnology & Oceanography

140

139

View full text Add to dashboard Cite

show abstract

“…Explanation by simplification Decision Tree/Prototype: [84,118,122] Feature relevance explanation Feature importance / contribution: [103,104,240,241] Visual explanation Variable importance / attribution: [104,241] [242,243] Support Vector Machines…”

Section: Ensembles and Multiple Classifier Systemsmentioning

confidence: 99%

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

et al. 2020

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In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if harnessed appropriately, may deliver the best of expectations over many application sectors across the field. For this to occur shortly in Machine Learning, the entire community stands in front of the barrier of explainability, an inherent problem of the latest techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI (namely, expert systems and rule based models). Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is widely acknowledged as a crucial feature for the practical deployment of AI models. The overview presented in this article examines the existing literature and contributions already done in the field of XAI, including a prospect toward what is yet to be reached. For this purpose we summarize previous efforts made to define explainability in Machine Learning, establishing a novel definition of explainable Machine Learning that covers such prior conceptual propositions with a major focus on the audience for which the explainability is sought. Departing from this definition, we propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at explaining Deep Learning methods for which a second dedicated taxonomy is built and examined in detail. This critical literature analysis serves as the motivating background for a series of challenges faced by XAI, such as the interesting crossroads of data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to the field of XAI with a thorough taxonomy that can serve as reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

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“…Also, determination of the significance of weakly important variables is difficult, as correlations among predictor variables will also influence the variable importance. 31 Therefore, the importance measure was calculated in conjunction with the following process.…”

Section: Variable Order Of Importancementioning

confidence: 99%

A geospatial model of ambient sound pressure levels in the contiguous United States

Mennitt

Sherrill

Fristrup

2014

The Journal of the Acoustical Society of America

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This paper presents a model that predicts measured sound pressure levels using geospatial features such as topography, climate, hydrology, and anthropogenic activity. The model utilizes random forest, a tree-based machine learning algorithm, which does not incorporate a priori knowledge of source characteristics or propagation mechanics. The response data encompasses 270 000 h of acoustical measurements from 190 sites located in National Parks across the contiguous United States. The explanatory variables were derived from national geospatial data layers and cross validation procedures were used to evaluate model performance and identify variables with predictive power. Using the model, the effects of individual explanatory variables on sound pressure level were isolated and quantified to reveal systematic trends across environmental gradients. Model performance varies by the acoustical metric of interest; the seasonal L50 can be predicted with a median absolute deviation of approximately 3 dB. The primary application for this model is to generalize point measurements to maps expressing spatial variation in ambient sound levels. An example of this mapping capability is presented for Zion National Park and Cedar Breaks National Monument in southwestern Utah.

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Interpretation of nonlinear relationships between process variables by use of random forests

Cited by 159 publications

References 24 publications

Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification

Patterns and drivers of deep chlorophyll maxima structure in 100 lakes: The relative importance of light and thermal stratification

Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

A geospatial model of ambient sound pressure levels in the contiguous United States

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