Divorce is one of the most common social issues in developed countries like
in the United States. Almost 50% of the recent marriages turn into an
involuntary divorce or separation. While it is evident that people vary to a
different extent, and even over time, an incident like Divorce does not
interrupt the individual's daily activities; still, Divorce has a severe effect
on the individual's mental health, and personal life. Within the scope of this
research, the divorce prediction was carried out by evaluating a dataset named
by the 'divorce predictor dataset' to correctly classify between married and
Divorce people using six different machine learning algorithms- Logistic
Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN),
Classification and Regression Trees (CART), Gaussian Na\"ive Bayes (NB), and,
Support Vector Machines (SVM). Preliminary computational results show that
algorithms such as SVM, KNN, and LDA, can perform that task with an accuracy of
98.57%. This work's additional novel contribution is the detailed and
comprehensive explanation of prediction probabilities using Local Interpretable
Model-Agnostic Explanations (LIME). Utilizing LIME to analyze test results
illustrates the possibility of differentiating between divorced and married
couples. Finally, we have developed a divorce predictor app considering ten
most important features that potentially affect couples in making decisions in
their divorce, such tools can be used by any one in order to identify their
relationship condition.

通过评估名为 “离婚预测数据集” 的数据集，使用六种不同的机器学习算法（Logistic Regression，Linear Discriminant Analysis，K-Nearest Neighbors，Classification and Regression Trees，Gaussian Naive Bayes 和 Support Vector Machines），进行离婚预测，并使用 LIME（Local Interpretable Model-Agnostic Explanations）对预测概率进行详细解释，最终开发了一款离婚预测应用程序，用于帮助人们判断他们的关系状况。

机器学习预测离婚：洞察与 LIME 可解释性

Divorce Prediction with Machine Learning: Insights and LIME  Interpretability

Neural network prediction probabilities and accuracy are often only
weakly-correlated. Inherent label ambiguity in training data for image
segmentation aggravates such miscalibration. We show that logit consistency
across stochastic transformations acts as a spatially varying regularizer that
prevents overconfident predictions at pixels with ambiguous labels. Our
boundary-weighted extension of this regularizer provides state-of-the-art
calibration for prostate and heart MRI segmentation.

本文提出一种基于随机转换的正则化方法，通过对训练数据中标签模糊的像素进行空间变化保持，来提高神经网络的预测准确性，实现前列腺和心脏 MRI 图像分割的最佳校准。