Pairwise Acquisition Prediction with SHAP Value Interpretation

Futagami, Katsuya; Fukazawa, Yusuke; Kapoor, Nakul; Kito, Tomomi

doi:10.2139/ssrn.3709094

Cited by 2 publications

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“…To interpret the RF outcome, we used SHAP values (Shapley Additive explanation 94). SHAP values are a way of interpreting machine learning models based on information theory (94,95). In a nutshell, the SHAP value is derived from a regression tree model for a given feature and prediction.…”

Section: Methodsmentioning

confidence: 99%

“…By plotting the values of predictor vs. the associated SHAP values, we obtain a response curve analogous to the effects of that predictor over the response variable (after controlling for the effect of all others). SHAP values are widely used in machine learning (97), economics (94), security (95), and ecology (98). We extracted SHAP values with the package fastshap (99) and built-in functions of the package xgboost in R.…”

Section: Methodsmentioning

confidence: 99%

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Prevalence and drivers of abrupt vegetation shifts in global drylands

Berdugo

Gaitán

Delgado‐Baquerizo

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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The constant provision of plant productivity is integral to supporting the liability of ecosystems and human wellbeing in global drylands. Drylands are paradigmatic examples of systems prone to experiencing abrupt changes in their functioning. Indeed, space-for-time substitution approaches suggest that abrupt changes in plant productivity are widespread, but this evidence is less clear using observational time series or experimental data at a large scale. Studying the prevalence and, most importantly, the unknown drivers of abrupt (rather than gradual) dynamical patterns in drylands may help to unveil hotspots of current and future dynamical instabilities in drylands. Using a 20-y global satellite-derived temporal assessment of dryland Normalized Difference Vegetation Index (NDVI), we show that 50% of all dryland ecosystems exhibiting gains or losses of NDVI are characterized by abrupt positive/negative temporal dynamics. We further show that abrupt changes are more common among negative than positive NDVI trends and can be found in global regions suffering recent droughts, particularly around critical aridity thresholds. Positive abrupt dynamics are found most in ecosystems with low seasonal variability or high aridity. Our work unveils the high importance of climate variability on triggering abrupt shifts in vegetation and it provides missing evidence of increasing abruptness in systems intensively managed by humans, with low soil organic carbon contents, or around specific aridity thresholds. These results highlight that abrupt changes in dryland dynamics are very common, especially for productivity losses, pinpoint global hotspots of dryland vulnerability, and identify drivers that could be targeted for effective dryland management.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prevalence and drivers of abrupt vegetation shifts in global drylands

Berdugo

Gaitán

Delgado‐Baquerizo

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…In the subsequent section, this paper discusses in detail the risks existing in the procedure of college students' entrepreneurship in the Republic of China and studies and analyzes various risks. e second step is to build the initial index framework of institutional scholars' entrepreneurial risk [9,10].…”

Section: Characteristics and Distribution Of College Students' Entrep...mentioning

confidence: 99%

Research on the College Students’ Venture Risk Assessment Model Based on the LightGBM Algorithm

Feng

Zhang

2022

Mobile Information Systems

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There are numerous risk factors in the school students’ entrepreneurial process. In order to advance the accuracy of school students’ entrepreneurial risk assessment within entrepreneurial practice background and reduce risk management factors, a model of school students’ entrepreneurial risk assessment grounded on the LightGBM algorithm is proposed. We construct the statistical probability density distribution and constraint parameter model of college students’ entrepreneurial risk distribution in an entrepreneurial practice environment. Moreover, we adopt the factor hierarchical classification technology of the evaluation index system to realize the framework model construction and big data fusion processing of college students’ entrepreneurial risk indicators in the entrepreneurial practice environment. Then, we extract the joint similarity feature set of college students’ entrepreneurial risk factors in the entrepreneurial practice environment and adopt the LightGBM feature detection and fuzzy matching technology. The self-adaptive optimization and fuzzy clustering analysis are realized in the procedure of school students’ entrepreneurial risk assessment under the entrepreneurial practice environment, and a fuzzy Bayesian network assessment model for school students’ entrepreneurial risk assessment under the entrepreneurial practice environment is constructed. The Delphi method is used to determine the pairwise influence relationship between factors, and the LightGBM algorithm is used to optimize the evaluation of college students’ entrepreneurial risk factors under the entrepreneurial practice environment and match rough set features. According to the comprehensive evaluation of entrepreneurial risk factors, the intelligent entrepreneurial risk assessment under the entrepreneurial practice environment is comprehended. The simulation outcomes confirm that the intelligence of college students’ entrepreneurial risk assessment in the entrepreneurial practice environment is improved, and the precision of risk assessment is greater than other approaches.

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Pairwise Acquisition Prediction with SHAP Value Interpretation

Cited by 2 publications

References 0 publications

Prevalence and drivers of abrupt vegetation shifts in global drylands

Prevalence and drivers of abrupt vegetation shifts in global drylands

Research on the College Students’ Venture Risk Assessment Model Based on the LightGBM Algorithm

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