In explainable artificial intelligence (XAI) research, the predominant focus
has been on interpreting models for experts and practitioners. Model agnostic
and local explanation approaches are deemed interpretable and sufficient in
many applications. However, in domains like healthcare, where end users are
patients without AI or domain expertise, there is an urgent need for model
explanations that are more comprehensible and instil trust in the model's
operations. We hypothesise that generating model explanations that are
narrative, patient-specific and global(holistic of the model) would enable
better understandability and enable decision-making. We test this using a
decision tree model to generate both local and global explanations for patients
identified as having a high risk of coronary heart disease. These explanations
are presented to non-expert users. We find a strong individual preference for a
specific type of explanation. The majority of participants prefer global
explanations, while a smaller group prefers local explanations. A task based
evaluation of mental models of these participants provide valuable feedback to
enhance narrative global explanations. This, in turn, guides the design of
health informatics systems that are both trustworthy and actionable.

生成全球模型解释将促进人们更好地理解和做出决策，从而引导设计可信和实用的健康信息系统。

使用决策树评估全局模型解释的人类可理解性

Evaluation of Human-Understandability of Global Model Explanations using  Decision Tree

Predicting the price of used vehicles is a more interesting and needed
problem by many users. Vehicle price prediction can be a challenging task due
to the high number of attributes that should be considered for accurate
prediction. The major step in the prediction process is the collection and
pre-processing of the data. In this project, python scripts were built to
normalize, standardize, and clean data to avoid unnecessary noise for machine
learning algorithms. The data set used in this project can be very valuable in
conducting similar research using different prediction techniques. Many
assumptions were made on the basis of the data set. The proposed system uses a
Decision tree model and Gradient boosting predictive model, which are combined
in other to get closed to accurate prediction, the proposed model was evaluated
and it gives a promising performance. The future price prediction of used
vehicles with the help of the same data set will comprise different models.

预测二手车价格是一个有趣且有需求的问题，本研究使用数据预处理和决策树模型等方法，进行二手车价格预测并取得了令人满意的结果。