https://terms-en.ai-term-hub.com/zh/tags/diagnostics/Recent content in Diagnostics on 中文AI术语词典Hugozh-cnSat, 18 Jul 2026 11:44:45 +0000https://terms-en.ai-term-hub.com/zh/terms/underfitting/Sat, 18 Jul 2026 11:37:26 +0000https://terms-en.ai-term-hub.com/zh/terms/underfitting/<h2 id="definition">Definition</h2> <p>当统计模型或机器学习算法无法准确近似将输入映射到输出的函数时，就会发生欠拟合。这通常是因为模型对于数据而言过于简单所致。</p> <h3 id="summary">Summary</h3> <p>机器学习模型未能捕捉到训练数据潜在趋势的状态。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>偏差-方差权衡</li> <li>模型复杂度</li> <li>训练误差</li> <li>特征工程</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>诊断模型性能不佳</li> <li>调整超参数</li> <li>选择适当的算法</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/regularization-%E6%AD%A3%E5%88%99%E5%8C%96/">Regularization (正则化)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/hyperparameter-tuning-%E8%B6%85%E5%8F%82%E6%95%B0%E8%B0%83%E4%BC%98/">Hyperparameter Tuning (超参数调优)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/generalization-%E6%B3%9B%E5%8C%96/">Generalization (泛化)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/learning_curve/Sat, 18 Jul 2026 11:23:53 +0000https://terms-en.ai-term-hub.com/zh/terms/learning_curve/<h2 id="definition">Definition</h2> <p>通常，学习曲线在y轴上显示训练得分和验证得分，在x轴上显示训练样本数量或迭代次数。它有助于诊断模型是否存在高偏差（欠拟合）或高方差（过拟合）问题。</p> <h3 id="summary">Summary</h3> <p>学习曲线将模型的性能指标与训练数据量或训练轮数进行绘图，以可视化学习进度。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>训练得分</li> <li>验证得分</li> <li>欠拟合与过拟合</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>诊断模型性能问题</li> <li>确定样本量需求</li> <li>监控训练收敛情况</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/validation-set-%E9%AA%8C%E8%AF%81%E9%9B%86/">Validation set (验证集)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/overfitting-%E8%BF%87%E6%8B%9F%E5%90%88/">Overfitting (过拟合)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/convergence-%E6%94%B6%E6%95%9B/">Convergence (收敛)</a></li> </ul>https://terms-en.ai-term-hub.com/zh/terms/autognostics/Sat, 18 Jul 2026 11:07:26 +0000https://terms-en.ai-term-hub.com/zh/terms/autognostics/<h2 id="definition">Definition</h2> <p>自诊断技术是指嵌入在智能系统中的自我监控和自我修复机制。它允许AI代理检测异常、诊断故障的根本原因，并可能进行自我修复。</p> <h3 id="summary">Summary</h3> <p>人工智能系统在不依赖人工干预的情况下，自我诊断其内部状态、性能问题或错误的能力。</p> <h2 id="key-concepts">Key Concepts</h2> <ul> <li>自我监控</li> <li>故障诊断</li> <li>自主性</li> <li>系统健康</li> </ul> <h2 id="use-cases">Use Cases</h2> <ul> <li>自愈式云基础设施</li> <li>机器人系统检测机械磨损</li> <li>AI模型内部识别数据漂移</li> </ul> <h2 id="related-terms">Related Terms</h2> <ul> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E8%87%AA%E6%84%88%E7%B3%BB%E7%BB%9F-self-healing-systems/">自愈系统 (Self-Healing Systems)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%8F%AF%E8%A7%82%E6%B5%8B%E6%80%A7-observability/">可观测性 (Observability)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/mlops-mlops/">MLOps (MLOps)</a></li> <li><a href="https://terms-en.ai-term-hub.com/en/terms/%E5%BC%82%E5%B8%B8%E6%A3%80%E6%B5%8B-anomaly-detection/">异常检测 (Anomaly Detection)</a></li> </ul>