Given the nonlinearity of the interaction between weather and soil variables,
a novel deep neural network regressor (DNNR) was carefully designed with
considerations to the depth, number of neurons of the hidden layers, and the
hyperparameters with their optimizations. Additionally, a new metric, the
average of absolute root squared error (ARSE) was proposed to address the
shortcomings of root mean square error (RMSE) and mean absolute error (MAE)
while combining their strengths. Using the ARSE metric, the random forest
regressor (RFR) and the extreme gradient boosting regressor (XGBR), were
compared with DNNR. The RFR and XGBR achieved yield errors of 0.0000294 t/ha,
and 0.000792 t/ha, respectively, compared to the DNNR(s) which achieved 0.0146
t/ha and 0.0209 t/ha, respectively. All errors were impressively small.
However, with changes to the explanatory variables to ensure generalizability
to unforeseen data, DNNR(s) performed best. The unforeseen data, different from
unseen data, is coined to represent sudden and unexplainable change to weather
and soil variables due to climate change. Further analysis reveals that a
strong interaction does exist between weather and soil variables. Using
precipitation and silt, which are strong-negatively and strong-positively
correlated with yield, respectively, yield was observed to increase when
precipitation was reduced and silt increased, and vice-versa.

通过设计深度神经网络回归器（DNNR），提出了一种新颖的 DNNR 模型，用于解决天气和土壤变量之间的非线性相互作用问题，同时引入了平均绝对平方根误差（ARSE）指标以综合评估预测准确性，在与随机森林回归器（RFR）和极端梯度提升回归器（XGBR）进行比较后，DNNR 在处理突发的气候和土壤变化数据时表现最佳，进一步的分析揭示了天气和土壤变量之间存在着强烈的相互作用。

小农户决策支持系统的深度神经网络玉米产量预测模型

Corn Yield Prediction Model with Deep Neural Networks for Smallholder  Farmer Decision Support System

In this paper we describe our approach to the arousal and valence track of
the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild
(ABAW). We extracted facial features using OpenFace and used them to train a
multiple output random forest regressor. Our approach performed comparable to
the baseline approach.

使用 OpenFace 提取面部特征，训练多重输出随机森林回归器，与基准方法表现相似，这是关于野外情感行为分析比赛的研究。

基于面部动作单元的连续情感随机森林回归

Random Forest Regression for continuous affect using Facial Action Units

Understanding the causes of crime is a longstanding issue in researcher's
agenda. While it is a hard task to extract causality from data, several linear
models have been proposed to predict crime through the existing correlations
between crime and urban metrics. However, because of non-Gaussian distributions
and multicollinearity in urban indicators, it is common to find controversial
conclusions about the influence of some urban indicators on crime. Machine
learning ensemble-based algorithms can handle well such problems. Here, we use
a random forest regressor to predict crime and quantify the influence of urban
indicators on homicides. Our approach can have up to 97% of accuracy on crime
prediction, and the importance of urban indicators is ranked and clustered in
groups of equal influence, which are robust under slightly changes in the data
sample analyzed. Our results determine the rank of importance of urban
indicators to predict crime, unveiling that unemployment and illiteracy are the
most important variables for describing homicides in Brazilian cities. We
further believe that our approach helps in producing more robust conclusions
regarding the effects of urban indicators on crime, having potential
applications for guiding public policies for crime control.

使用随机森林回归器对城市指标对犯罪的影响进行了研究，发现失业率和文盲率是描述巴西城市凶杀案的最重要变量，这种机器学习的方法有望帮助制定更有针对性的公共政策。