机器学习：中英双语指南

Validation status / 复核结论： The original explanation was broadly correct and well structured. This edition corrects several boundary cases, sharpens algorithm-selection guidance, and distinguishes learning paradigms, task types, model families, and deployment requirements.
原说明整体正确、结构完整。本修订版进一步纠正边界问题，并明确区分学习范式、任务类型、模型家族及生产部署条件。

Machine learning should not be organized as one flat list. Four different layers are involved:

机器学习不能被理解成一个平铺的算法清单，而应分成四个层次：

1. Learning paradigm / 学习范式 — how the model receives feedback.
2. Task type / 任务类型 — what output the model must produce.
3. Algorithm or model family / 算法或模型家族 — how the relationship is learned.
4. Operational system / 生产系统 — how the model is evaluated, deployed, monitored, and used.

The basic pipeline is:

Business question → Learning paradigm → Task → Algorithm → Evaluation → Deployment → Monitoring
业务问题 → 学习范式 → 任务类型 → 算法选择 → 评估 → 部署 → 监控

Examples:

• Supervised learning / 监督学习 is a learning paradigm.
• Classification / 分类 is a task.
• Random forest / 随机森林 is an algorithm family.
• F1, calibration, latency, and drift monitoring / F1、概率校准、延迟和漂移监控 are evaluation and operational concerns.

One algorithm can solve several tasks. Random forests support classification and regression. Neural networks can support classification, regression, generation, representation learning, and reinforcement learning.

同一算法可以解决多种任务。例如随机森林可以分类，也可以回归；神经网络可以用于分类、回归、生成、表示学习及强化学习。

These categories can overlap. A system may use self-supervised pretraining, supervised fine-tuning, active labeling, and online monitoring in the same lifecycle.

这些范式并非互斥。一个系统可以先进行自监督预训练，再做监督微调，同时采用主动标注，并在部署后进行在线监控。

Supervised data contain input features (X) and known targets (y):

监督学习数据包含特征 (X) 和已知目标 (y)：

[ f(X) \rightarrow y ]

Classification predicts discrete categories or class probabilities.
分类用于预测离散类别或类别概率。

For a contract-win model, precision matters when bids are expensive and resources must be concentrated; recall matters when missing a viable opportunity is costly. If the model reports “70% chance of winning,” probability calibration must also be tested.

对于合同中标模型，如果投标成本高、资源需要集中，Precision更重要；如果漏掉可行机会的代价高，Recall更重要。如果模型输出“70%中标概率”，还必须检验概率校准。

Regression predicts a continuous number, such as cost, demand, duration, or contract value.
回归预测连续数值，例如成本、需求量、工期或合同金额。

Common metrics include MAE, MSE, RMSE, (R^2), MAPE, and pinball loss. MAPE becomes unstable when actual values are zero or close to zero. (R^2) alone does not prove accuracy, causality, or operational usefulness.

常见指标包括MAE、MSE、RMSE、(R^2)、MAPE及Pinball Loss。真实值为零或接近零时，MAPE会失真；单独的(R^2)不能证明模型准确、具有因果性或具备业务价值。

Ranking predicts relative order rather than a standalone category. It is useful for contract-opportunity prioritization, search, recommendations, candidate retrieval, and risk triage.

排序学习预测对象的相对顺序，而不是单独类别。适用于合同机会优先级、搜索、推荐、候选对象检索及风险排序。

Common models include RankNet, LambdaRank, LambdaMART, pairwise ranking, and boosted-tree ranking. Training requires query/user/task groups and relevance or preference labels. Evaluate with NDCG, MAP, or MRR. Historical click data require correction for exposure and position bias.

常用模型包括RankNet、LambdaRank、LambdaMART、成对排序及提升树排序。训练需要查询、用户或任务分组，以及相关性或偏好标签。通常使用NDCG、MAP或MRR评估；历史点击数据还要控制曝光偏差和位置偏差。

Time order must be preserved. Typical applications include budget execution, expenditure, inventory, demand, staffing, and server-load forecasts.

时间顺序必须保留。典型应用包括预算执行、支出、库存、需求、人员及服务器负载预测。

Use walk-forward or rolling-origin validation. A random row split usually leaks temporal information. Every feature must be available at the actual forecast time. More complex deep models do not automatically beat seasonal baselines or boosted trees.

应使用Walk-forward或Rolling-origin验证。随机拆分行通常造成时间泄漏。所有特征必须在真实预测时点可获得。复杂深度模型不会自动优于季节基线或提升树。

• Survival analysis / 生存分析: predicts time to an event while handling censored observations; examples include equipment failure, attrition, and project delay. Models include Cox regression and random survival forests.
• Structured prediction / 结构化预测: produces sequences or structured outputs, such as named entities, translations, speech transcripts, image segmentation, or document fields. Models include CRFs, transformers, and U-Net-style architectures.
• Count modeling / 计数建模: predicts event counts with Poisson, negative-binomial, or related models when ordinary regression assumptions are unsuitable.

Unsupervised learning discovers structure without human-provided targets. Results generally require stronger domain interpretation because there is no definitive label-based accuracy score.

无监督学习在没有人工目标标签的情况下发现结构。由于不存在天然的标签准确率，结果通常更依赖领域解释。

Silhouette, Davies–Bouldin, and Calinski–Harabasz scores assess geometric properties, not whether the clusters are real business segments. Validate stability, interpretability, reproducibility, and downstream usefulness.

Silhouette、Davies–Bouldin及Calinski–Harabasz衡量的是几何结构，不能证明群组是真实业务分群。还必须验证稳定性、可解释性、可重复性及下游价值。

Anomaly detection is a task, not exclusively an unsupervised paradigm. It may be:

异常检测是一种任务，并不专属于无监督学习。它可以是：

• supervised, when reliable anomaly labels exist;
• semi-supervised or one-class, when mostly normal examples are available;
• unsupervised, when labels do not exist.

An unusual record is not automatically fraud, error, or wrongdoing. The model flags deviation; investigation establishes meaning.

异常记录并不自动等于欺诈、错误或违规。模型只负责发现偏离，业务调查才能确定含义。

Association-rule methods such as Apriori and FP-Growth identify frequent co-occurrence. Key measures are support, confidence, and lift:

Apriori、FP-Growth等关联规则方法识别高频共现。关键指标包括支持度、置信度和提升度：

[ Support(A\rightarrow B)=P(A\cap B) ]

[ Confidence(A\rightarrow B)=P(B|A) ]

[ Lift(A\rightarrow B)=\frac{P(B|A)}{P(B)} ]

Association is not causation. High-confidence rules can simply reflect a very common item.

关联不等于因果。高置信度规则可能只是因为某项目本身非常常见。

Topic discovery methods include LDA, NMF, BERTopic, and embedding-plus-clustering pipelines. Good tokenization, sufficient documents, multilingual support, stable topics, and expert interpretation are required.

主题发现可采用LDA、NMF、BERTopic或“嵌入＋聚类”。需要良好分词、足够文档、多语言支持、稳定主题及专家解释。

Use it when labels are expensive but unlabeled data are abundant and come from a distribution similar to deployment data. Methods include pseudo-labeling, self-training, label propagation, consistency regularization, FixMatch, and MixMatch.

当标签昂贵、无标签数据丰富且与部署数据分布相近时，可使用半监督学习。常用方法包括伪标签、自训练、标签传播、一致性正则化、FixMatch及MixMatch。

Success requires:

1. A representative labeled seed set / 有代表性的初始标签集。
2. A fully human-labeled holdout test set / 完全由人工标注的独立测试集。
3. Confidence thresholds and class-balance checks / 置信度阈值与类别平衡检查。
4. Monitoring for confirmation bias / 监控确认偏差。
5. Similarity between unlabeled and deployment distributions / 无标签数据与部署数据分布相似。

Self-supervised learning creates targets from raw data: next-token prediction, masked-token reconstruction, contrastive learning, masked-image reconstruction, or speech-segment prediction. It powers much of modern LLM, vision, and speech pretraining.

自监督学习从原始数据自动构造训练目标，例如下一Token预测、遮挡Token重建、对比学习、遮挡图像重建或语音片段预测，是现代LLM、视觉及语音预训练的重要基础。

Effective pretraining requires broad, representative, cleaned data; a suitable objective; sufficient model capacity and compute; deduplication; and privacy, copyright, bias, and safety controls. The pretrained representation still needs task-specific evaluation and often prompting, retrieval, adapters, or fine-tuning.

有效预训练需要广泛、代表性强且经过清理的数据，合适的训练目标，足够的模型容量与算力，数据去重，以及隐私、版权、偏见和安全控制。预训练后仍需进行具体任务评估，并可能配合提示、检索、适配器或微调。

Reinforcement learning learns a policy through interaction. It is most appropriate when actions affect future states and rewards—not merely when a labeled prediction is needed.

强化学习通过与环境交互学习策略。只有当行动会影响未来状态和奖励时才特别适合；如果只需要有标签预测，通常无需强化学习。

Core concepts are agent, environment, state, action, reward, policy, value, exploration, and exploitation. The objective is commonly a discounted return:

核心概念包括智能体、环境、状态、动作、奖励、策略、价值、探索和利用。目标通常是最大化折扣累计回报：

[ G_t=\sum_{k=0}^{\infty}\gamma^k r_{t+k+1} ]

Effective RL needs a well-defined state/action/reward system, adequate exploration or representative offline coverage, safe experimentation or a realistic simulator, reliable reward attribution, and guardrails against reward hacking. In high-stakes domains, off-policy evaluation, uncertainty analysis, and human oversight are essential.

有效强化学习需要清晰定义状态、动作和奖励，需要充分探索或有代表性的离线覆盖，需要安全试验环境或逼真模拟器，并需要可靠的奖励归因及防止奖励投机。在高风险领域，还必须进行离线策略评估、不确定性分析及人工监督。