We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile and Tunisia against climate hazards. We show how group structures can be considered and how interactions can be found in high-dimensional datasets using a novel 2-step boosting approach. The advantages and efficacy of the proposed method are shown and discussed. Results indicate that the presence of interaction effects only improves predictive power when included in two-step boosting. The most important variable in predicting all types of vulnerabilities are natural assets. Other important variables are the type of irrigation, economic assets and the presence of crop damage of near farms.

本文研究解释性提升算法和基于岭正则化广义线性模型的应用，帮助分析高维环境数据，通过智利和突尼斯农民的环境、社会、人类和生物物理数据进行预测他们在气候灾害中的财务脆弱性，使用新的2步提升方法在高维数据集中处理组结构和交互作用，并显示了该方法的优点和功效。结果表明，仅在两步提升中包含交互效应会提高预测能力。在预测各种类型的脆弱性中，最重要的变量是自然资产，其他重要变量包括灌溉类型、经济资产和附近农场的作物损伤情况。

使用可解释增强算法对环境和农业数据进行建模