> For the complete documentation index, see [llms.txt](https://docs.docbits.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.docbits.com/chinese/guan-li-yu-she-zhi/settings/global-settings/document-types/model-training/testing-the-model.md).
# 测试模型
## 运行分类测试以评估模型准确性和运行就绪情况的步骤
要测试训练好的模型并评估其准确性和运行就绪情况，请按照以下步骤操作：
**准备测试数据：**
* 收集具有代表性的测试数据样本，涵盖模型在实际使用中需要处理的不同文档类型和场景。确保测试数据质量高且标注正确。
**运行分类测试：**
* 使用准备好的测试数据运行分类测试。
* 将测试数据输入模型，并让模型预测文档类别。
设置
* 添加新的分类规则，或编辑现有规则。
设置：分类和提取
配置分类规则
**评估模型准确性：**
* 将模型的预测结果与测试数据的实际分类进行比较。计算准确率、精确率、召回率和 F1 分数等指标，以评估模型的性能。这些指标有助于了解模型对文档分类的效果及其可靠性。
**分析错误：**
* 检查模型在测试数据分类过程中出现的错误，并分析原因。识别错误中的模式或趋势，并在必要时调整模型以提升其性能。
**优化模型**：
* 根据分类测试和错误分析的结果，您可以通过添加训练数据、调整训练参数或更改模型架构来优化模型。重复测试流程，检查优化是否提升了模型性能。
**记录结果：**
* 记录分类测试的结果，以及对模型所做的任何调整或优化。这有助于跟踪模型随时间的进展，并确保其持续改进。
定期运行分类测试并评估模型性能，可以确保模型适用于生产环境并提供准确结果。
---
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