翻译：
Model Evaluation Methods What are the methods when conducting AI (Artificial intelligence) model evaluation?
模型评估方法是把数据切分并反复验证以估算模型泛化能力的一组做法，构成上包括留出法（通常70%-80%训练、余下30%-20%验证）、交叉验证法与自助法，解决的是数据量有限时如何获得可靠评估结论的问题，并不解决模型训练本身的效果问题，也无法免除样本选择偏差带来的风险。
When conducting AI (artificial intelligence) model evaluation, there are several approaches:
1. Set aside method: Set aside method divides the data set into training set and verification set, usually 70-80% of the data set is used for training, and the remaining 30-20% is used for verification. This method is simple and feasible, but when the amount of data is small, the partitioned verification set may not be representative enough, so the evaluation results may not be reliable.
2. Cross-validation method: The cross-validation method is to randomly divide the data set into training set and verification set several times, obtain the results of multiple models and take the average value to evaluate the performance of the model. This method can effectively reduce the randomness caused by data set partitioning and is more reliable.
3. Self-help method: self-help method is to select samples from the data set to be put back for training. In each training, the selected samples are formed into a random subset for training the model, and the remaining samples are used for model evaluation. This method is more effective in the case of small data sets, but the problem of sample selection bias needs to be considered.
