Performance Evaluation of Neural Network Decision Models
针对两类分类问题中状态频率不均衡导致模型评估困难的问题，研究了训练样本设计和绩效指标选择，通过比较神经网络与统计模型在识别成功新创企业决策中的表现来探讨这两个问题。
:Recently, promising results with neural networks have been reported for two-group classification problems such as bankruptcy prediction and thrift failures. Such applications are usually characterized by unequal frequencies of the two states of interest. This creates a major obstacle to effective performance evaluation of various decision models. Critical issues affecting the comparison include training sample design and the use of an appropriate performance metric. This paper addresses these two issues by comparing the performance of neural networks with that of statistical models for the decision problem of identifying successful new ventures.