Principles and practice of explainable machine learning

V Belle, I Papantonis - Frontiers in big Data, 2021 - frontiersin.org
Artificial intelligence (AI) provides many opportunities to improve private and public life.
Discovering patterns and structures in large troves of data in an automated manner is a core …

Artificial intelligence and machine learning in pathology: the present landscape of supervised methods

HH Rashidi, NK Tran, EV Betts… - Academic …, 2019 - journals.sagepub.com
Increased interest in the opportunities provided by artificial intelligence and machine
learning has spawned a new field of health-care research. The new tools under …

[BOOK][B] Habitat suitability and distribution models: with applications in R

A Guisan, W Thuiller, NE Zimmermann - 2017 - books.google.com
This book introduces the key stages of niche-based habitat suitability model building,
evaluation and prediction required for understanding and predicting future patterns of …

The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus

MUG Kraemer, ME Sinka, KA Duda, AQN Mylne… - elife, 2015 - elifesciences.org
Dengue and chikungunya are increasing global public health concerns due to their rapid
geographical spread and increasing disease burden. Knowledge of the contemporary …

A working guide to boosted regression trees

J Elith, JR Leathwick, T Hastie - Journal of animal ecology, 2008 - Wiley Online Library
Summary 1 Ecologists use statistical models for both explanation and prediction, and need
techniques that are flexible enough to express typical features of their data, such as …

Soil quality both increases crop production and improves resilience to climate change

L Qiao, X Wang, P Smith, J Fan, Y Lu, B Emmett… - Nature Climate …, 2022 - nature.com
Interactions between soil quality and climate change may influence the capacity of
croplands to produce sufficient food. Here, we address this issue by using a new dataset of …

Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score

PM Ridker, JE Buring, N Rifai, NR Cook - Jama, 2007 - jamanetwork.com
ContextDespite improved understanding of atherothrombosis, cardiovascular prediction
algorithms for women have largely relied on traditional risk factors. ObjectiveTo develop and …

GIS-based groundwater potential map** using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran

SA Naghibi, HR Pourghasemi, B Dixon - Environmental monitoring and …, 2016 - Springer
Groundwater is considered one of the most valuable fresh water resources. The main
objective of this study was to produce groundwater spring potential maps in the Koohrang …

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

Y Zhu, L Zhou, C **e, GJ Wang, TV Nguyen - International Journal of …, 2019 - Elsevier
In recent years, financial institutions (FIs) have tentatively utilized supply chain finance (SCF)
as a means of solving the financing issues of small and medium-sized enterprises (SMEs) …

Forest productivity increases with evenness, species richness and trait variation: a global meta‐analysis

Y Zhang, HYH Chen, PB Reich - Journal of ecology, 2012 - Wiley Online Library
Although there is ample support for positive species richness–productivity relationships in
planted grassland experiments, a recent 48‐site study found no diversity–productivity …