Machine learning and deep learning—A review for ecologists
The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …
Big-data science in porous materials: materials genomics and machine learning
By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …
Interpretable machine learning–a brief history, state-of-the-art and challenges
We present a brief history of the field of interpretable machine learning (IML), give an
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
overview of state-of-the-art interpretation methods and discuss challenges. Research in IML …
Evaluating post-hoc explanations for graph neural networks via robustness analysis
This work studies the evaluation of explaining graph neural networks (GNNs), which is
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
crucial to the credibility of post-hoc explainability in practical usage. Conventional evaluation …
Explainable machine learning in deployment
Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …
model behavior by using various methods such as feature importance scores, counterfactual …
Explaining by removing: A unified framework for model explanation
Researchers have proposed a wide variety of model explanation approaches, but it remains
unclear how most methods are related or when one method is preferable to another. We …
unclear how most methods are related or when one method is preferable to another. We …
Problems with Shapley-value-based explanations as feature importance measures
Game-theoretic formulations of feature importance have become popular as a way to"
explain" machine learning models. These methods define a cooperative game between the …
explain" machine learning models. These methods define a cooperative game between the …
Understanding global feature contributions with additive importance measures
Understanding the inner workings of complex machine learning models is a long-standing
problem and most recent research has focused on local interpretability. To assess the role of …
problem and most recent research has focused on local interpretability. To assess the role of …
Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach
As the world's leading corn producer, the United States supplies more than 30% of the
global corn production. Accurate and timely estimation of corn yield is therefore essential for …
global corn production. Accurate and timely estimation of corn yield is therefore essential for …
General pitfalls of model-agnostic interpretation methods for machine learning models
An increasing number of model-agnostic interpretation techniques for machine learning
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …
(ML) models such as partial dependence plots (PDP), permutation feature importance (PFI) …