---
title: "机器学习与预测分析"
canonical: https://wavect.io/zh/services/machine-learning/
language: zh
description: "为预测、分类和异常检测开发机器学习模型。Wavect 检查数据，比较简单基线，并将经过评估的预测接入实际业务流程。"
image: "https://wavect.io/img/general/bak/open_graph_preview.jpg"
---

机器学习

# 让预测接受真实业务决策的检验。

有用的预测应该改变下一步行动。我们先明确业务决策，检查现有数据是否足以支持它，再构建模型及其使用所需的集成。简单的基线方法始终是认真比较的选项。

合作范围

## 一个决策、一条基线、一个能够检验的模型。

1. **定义成功** 约定行动、目标变量和不同错误的成本。
2. **检查证据** 检查历史覆盖、信息泄漏、缺失值和有代表性的留出测试。
3. **使用预测** 将获批模型接入流程，并定义监控和回退方式。

直接说明

本服务开发并评估预测、分类和异常检测模型，将获得认可的预测接入业务流程。起点是数据适用性、可衡量的基线，以及不同错误所造成的业务成本。

适合以下需求

- 拥有历史数据，希望改善重复性决策的团队
- 需要预测、评分或异常信号，并希望了解其测量结果和边界的产品

其他需求路径

- 构建聊天机器人或文档检索界面
- 通过报表展示已经发生的情况

## 从实际操作向前追溯

需求预测应当支持补货决定，异常分数应当帮助分派复核。我们明确由谁采取行动、何时需要预测，以及预测错误后如何处理。目标必须能依据预测当时已经可用的信息进行评估。报表和源数据管道如需建设，仍由独立的 [数据工程](/zh/services/data-engineering/) 范围承接。

## 检查历史能否代表未来的使用条件

我们检查数据覆盖、业务规则变化、缺失结果和来自未来的信息。预测任务可能需要按时间划分训练与测试；相关客户或设备可能需要放在同一分组，避免评估失真。评估会说明数据能够支持哪些群体和条件，以及哪些部分应当先补充数据而不是继续训练。

## 与当前的决策方法比较

增加复杂度之前，先建立简单基线。评估包括业务相关错误、重要子群体和不确定性，阈值按照预定行动确定。总体指标更好，但在高成本场景中更差的模型，也可能不被采用。交付结果是一份可以检查的比较，包含适用边界，而不是一个孤立的准确率数字。

## 示例验收：不使用未来信息预测需求

一个示例测试仅使用截止日之前的历史数据训练模型，再预测之后的时期。结果与季节性基线比较，并按产品组拆分。刚上市的商品被标记为证据不足。验收记录包含误差、运营影响，以及模型是否适合预定的采购或补货流程。

## 让部署具有可回退的路径

我们交付数据转换、模型文件、评估记录和预测接口。批量计算可能已经足够；是否需要实时服务，由业务流程决定。双方明确回退行为、监控方式和重新训练的决策负责人。持续的版本管理、发布和数据漂移处理可通过 [MLOps](/zh/services/mlops/) 继续实施。

## 开始前的常见问题

### 需要多少数据？

不存在普遍适用的最低行数。目标定义、变化程度、结果质量和历史代表性更重要。我们先评估这些因素，再提出训练方案。

### 一定会开发定制模型吗？

不会。合适的规则、现有模型和简单统计方法都会纳入比较。最终方案需要证明其额外运行成本是合理的。

### 预测可以自动触发决策吗？

可以在约定的决策政策内执行。阈值、复核路径和权限与模型分数分别定义，尤其是在错误操作会造成明显损失时。

### 如果模型没有超过基线怎么办？

评估仍能帮助作出选择：改善数据、调整目标、保留现有方法或停止该方向。是否继续实现，由双方根据评估证据决定。

## 选择合适的下一步

- [准备可靠的分析数据](/zh/services/data-engineering/)
- [持续管理训练后的模型](/zh/services/mlops/)
- [评估基于图像的应用场景](/zh/services/computer-vision/)

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