论文标题
机器学习和深度学习 - 对生态学家的评论
Machine Learning and Deep Learning -- A review for Ecologists
论文作者
论文摘要
1。近年来，机器学习（ML），深度学习（DL）和人工智能（AI）的流行急剧上升。尽管流行了这种流行，但ML和DL算法的内部运作通常被认为是不透明的，并且它们与经典数据分析工具的关系仍在争论中。 2。尽管通常认为ML和DL Excel主要是在做出预测时，但ML和DL也可以用于传统上使用统计模型来解决的分析任务。此外，关于ML的最新讨论和评论主要关注DL，而错过了具有不同优势和一般原则的ML算法财富的综合。 3。在这里，我们提供了ML和DL领域的全面概述，首先总结了其历史发展，现有算法家庭，与传统统计工具的差异以及通用ML原则。然后，我们讨论为什么以及何时ML和DL模型在预测任务上表现出色，以及它们可以为推理提供传统统计方法的替代方法，从而突出了当前和新兴的生态问题应用程序。最后，我们总结了新兴趋势，例如科学和因果ML，可解释的AI以及负责的AI，这些AI可能会在未来显着影响生态数据分析。 4。我们得出的结论是，ML和DL是用于预测建模和数据分析的强大新工具。与统计模型相比，ML和DL算法的出色性能可以通过其更高的灵活性和自动数据依赖性复杂性优化来解释。但是，由于ML和DL方法对预测的重点，它们在因果推理中的使用仍引起了这些模型的解释挑战。尽管如此，我们希望ML和DL成为E＆E中必不可少的工具，与其他传统统计工具相当。
1. 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 and DL algorithms are often perceived as opaque, and their relationship to classical data analysis tools remains debated. 2. Although it is often assumed that ML and DL excel primarily at making predictions, ML and DL can also be used for analytical tasks traditionally addressed with statistical models. Moreover, most recent discussions and reviews on ML focus mainly on DL, missing out on synthesizing the wealth of ML algorithms with different advantages and general principles. 3. Here, we provide a comprehensive overview of the field of ML and DL, starting by summarizing its historical developments, existing algorithm families, differences to traditional statistical tools, and universal ML principles. We then discuss why and when ML and DL models excel at prediction tasks and where they could offer alternatives to traditional statistical methods for inference, highlighting current and emerging applications for ecological problems. Finally, we summarize emerging trends such as scientific and causal ML, explainable AI, and responsible AI that may significantly impact ecological data analysis in the future. 4. We conclude that ML and DL are powerful new tools for predictive modeling and data analysis. The superior performance of ML and DL algorithms compared to statistical models can be explained by their higher flexibility and automatic data-dependent complexity optimization. However, their use for causal inference is still disputed as the focus of ML and DL methods on predictions creates challenges for the interpretation of these models. Nevertheless, we expect ML and DL to become an indispensable tool in E&E, comparable to other traditional statistical tools.
