【术语记录】机器学习

Evernote Export

• 实例：要对其进行预测的事物。例如，实例可以是一个网页，您希望将其分类为“与猫相关”或“与猫无关”。
• 标签：预测任务的答案，它可以是由机器学习系统生成的答案，也可以是训练数据中提供的正确答案。例如，某个网页的标签可能是“与猫相关”。
• 特征：预测任务中使用的实例的属性。例如，某个网页可能具有“包含字词‘猫’”这一特征。特征列：一组相关特征，例如用户可能居住的所有国家/地区的集合。样本的特征列中可能包含一个或多个特征。
• 特征列是 Google 专用的术语。特征列在 Yahoo/Microsoft 使用的 VM 系统中被称为“命名空间”或场。
• 样本：一个实例（及其特征）和一个标签。模型：预测任务的统计表示法。您使用样本训练一个模型，然后使用该模型进行预测。
• 指标：您关心的一个数值。也许（但不一定）可以直接得到优化。
• 目标：算法尝试优化的一种指标。
• 管道：机器学习算法的基础架构。管道包括从前端收集数据、将数据放入训练数据文件、训练一个或多个模型以及将模型运用到生产环境。
• 点击率：点击广告中的链接的网页访问者所占的百分比。

更多细节关于机器学习术语

%0A*%20**%E5%AE%9E%E4%BE%8B**%EF%BC%9A%E8%A6%81%E5%AF%B9%E5%85%B6%E8%BF%9B%E8%A1%8C%E9%A2%84%E6%B5%8B%E7%9A%84%E4%BA%8B%E7%89%A9%E3%80%82%E4%BE%8B%E5%A6%82%EF%BC%8C%E5%AE%9E%E4%BE%8B%E5%8F%AF%E4%BB%A5%E6%98%AF%E4%B8%80%E4%B8%AA%E7%BD%91%E9%A1%B5%EF%BC%8C%E6%82%A8%E5%B8%8C%E6%9C%9B%E5%B0%86%E5%85%B6%E5%88%86%E7%B1%BB%E4%B8%BA%E2%80%9C%E4%B8%8E%E7%8C%AB%E7%9B%B8%E5%85%B3%E2%80%9D%E6%88%96%E2%80%9C%E4%B8%8E%E7%8C%AB%E6%97%A0%E5%85%B3%E2%80%9D%E3%80%82%0A*%20**%E6%A0%87%E7%AD%BE**%EF%BC%9A%E9%A2%84%E6%B5%8B%E4%BB%BB%E5%8A%A1%E7%9A%84%E7%AD%94%E6%A1%88%EF%BC%8C%E5%AE%83%E5%8F%AF%E4%BB%A5%E6%98%AF%E7%94%B1%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%B3%BB%E7%BB%9F%E7%94%9F%E6%88%90%E7%9A%84%E7%AD%94%E6%A1%88%EF%BC%8C%E4%B9%9F%E5%8F%AF%E4%BB%A5%E6%98%AF%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%E4%B8%AD%E6%8F%90%E4%BE%9B%E7%9A%84%E6%AD%A3%E7%A1%AE%E7%AD%94%E6%A1%88%E3%80%82%E4%BE%8B%E5%A6%82%EF%BC%8C%E6%9F%90%E4%B8%AA%E7%BD%91%E9%A1%B5%E7%9A%84%E6%A0%87%E7%AD%BE%E5%8F%AF%E8%83%BD%E6%98%AF%E2%80%9C%E4%B8%8E%E7%8C%AB%E7%9B%B8%E5%85%B3%E2%80%9D%E3%80%82%0A*%20**%E7%89%B9%E5%BE%81**%EF%BC%9A%E9%A2%84%E6%B5%8B%E4%BB%BB%E5%8A%A1%E4%B8%AD%E4%BD%BF%E7%94%A8%E7%9A%84%E5%AE%9E%E4%BE%8B%E7%9A%84%E5%B1%9E%E6%80%A7%E3%80%82%E4%BE%8B%E5%A6%82%EF%BC%8C%E6%9F%90%E4%B8%AA%E7%BD%91%E9%A1%B5%E5%8F%AF%E8%83%BD%E5%85%B7%E6%9C%89%E2%80%9C%E5%8C%85%E5%90%AB%E5%AD%97%E8%AF%8D%E2%80%98%E7%8C%AB%E2%80%99%E2%80%9D%E8%BF%99%E4%B8%80%E7%89%B9%E5%BE%81%E3%80%82%E7%89%B9%E5%BE%81%E5%88%97%EF%BC%9A%E4%B8%80%E7%BB%84%E7%9B%B8%E5%85%B3%E7%89%B9%E5%BE%81%EF%BC%8C%E4%BE%8B%E5%A6%82%E7%94%A8%E6%88%B7%E5%8F%AF%E8%83%BD%E5%B1%85%E4%BD%8F%E7%9A%84%E6%89%80%E6%9C%89%E5%9B%BD%E5%AE%B6%2F%E5%9C%B0%E5%8C%BA%E7%9A%84%E9%9B%86%E5%90%88%E3%80%82%E6%A0%B7%E6%9C%AC%E7%9A%84%E7%89%B9%E5%BE%81%E5%88%97%E4%B8%AD%E5%8F%AF%E8%83%BD%E5%8C%85%E5%90%AB%E4%B8%80%E4%B8%AA%E6%88%96%E5%A4%9A%E4%B8%AA%E7%89%B9%E5%BE%81%E3%80%82%0A*%20**%E7%89%B9%E5%BE%81%E5%88%97**%E6%98%AF%20Google%20%E4%B8%93%E7%94%A8%E7%9A%84%E6%9C%AF%E8%AF%AD%E3%80%82%E7%89%B9%E5%BE%81%E5%88%97%E5%9C%A8%20Yahoo%2FMicrosoft%20%E4%BD%BF%E7%94%A8%E7%9A%84%20VM%20%E7%B3%BB%E7%BB%9F%E4%B8%AD%E8%A2%AB%E7%A7%B0%E4%B8%BA%E2%80%9C%E5%91%BD%E5%90%8D%E7%A9%BA%E9%97%B4%E2%80%9D%E6%88%96%E5%9C%BA%E3%80%82%0A*%20**%E6%A0%B7%E6%9C%AC**%EF%BC%9A%E4%B8%80%E4%B8%AA%E5%AE%9E%E4%BE%8B%EF%BC%88%E5%8F%8A%E5%85%B6%E7%89%B9%E5%BE%81%EF%BC%89%E5%92%8C%E4%B8%80%E4%B8%AA%E6%A0%87%E7%AD%BE%E3%80%82%E6%A8%A1%E5%9E%8B%EF%BC%9A%E9%A2%84%E6%B5%8B%E4%BB%BB%E5%8A%A1%E7%9A%84%E7%BB%9F%E8%AE%A1%E8%A1%A8%E7%A4%BA%E6%B3%95%E3%80%82%E6%82%A8%E4%BD%BF%E7%94%A8%E6%A0%B7%E6%9C%AC%E8%AE%AD%E7%BB%83%E4%B8%80%E4%B8%AA%E6%A8%A1%E5%9E%8B%EF%BC%8C%E7%84%B6%E5%90%8E%E4%BD%BF%E7%94%A8%E8%AF%A5%E6%A8%A1%E5%9E%8B%E8%BF%9B%E8%A1%8C%E9%A2%84%E6%B5%8B%E3%80%82%0A*%20**%E6%8C%87%E6%A0%87**%EF%BC%9A%E6%82%A8%E5%85%B3%E5%BF%83%E7%9A%84%E4%B8%80%E4%B8%AA%E6%95%B0%E5%80%BC%E3%80%82%E4%B9%9F%E8%AE%B8%EF%BC%88%E4%BD%86%E4%B8%8D%E4%B8%80%E5%AE%9A%EF%BC%89%E5%8F%AF%E4%BB%A5%E7%9B%B4%E6%8E%A5%E5%BE%97%E5%88%B0%E4%BC%98%E5%8C%96%E3%80%82%0A*%20**%E7%9B%AE%E6%A0%87**%EF%BC%9A%E7%AE%97%E6%B3%95%E5%B0%9D%E8%AF%95%E4%BC%98%E5%8C%96%E7%9A%84%E4%B8%80%E7%A7%8D%E6%8C%87%E6%A0%87%E3%80%82%0A*%20**%E7%AE%A1%E9%81%93**%EF%BC%9A%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E7%AE%97%E6%B3%95%E7%9A%84%E5%9F%BA%E7%A1%80%E6%9E%B6%E6%9E%84%E3%80%82%E7%AE%A1%E9%81%93%E5%8C%85%E6%8B%AC%E4%BB%8E%E5%89%8D%E7%AB%AF%E6%94%B6%E9%9B%86%E6%95%B0%E6%8D%AE%E3%80%81%E5%B0%86%E6%95%B0%E6%8D%AE%E6%94%BE%E5%85%A5%E8%AE%AD%E7%BB%83%E6%95%B0%E6%8D%AE%E6%96%87%E4%BB%B6%E3%80%81%E8%AE%AD%E7%BB%83%E4%B8%80%E4%B8%AA%E6%88%96%E5%A4%9A%E4%B8%AA%E6%A8%A1%E5%9E%8B%E4%BB%A5%E5%8F%8A%E5%B0%86%E6%A8%A1%E5%9E%8B%E8%BF%90%E7%94%A8%E5%88%B0%E7%94%9F%E4%BA%A7%E7%8E%AF%E5%A2%83%E3%80%82%0A*%20**%E7%82%B9%E5%87%BB%E7%8E%87**%EF%BC%9A%E7%82%B9%E5%87%BB%E5%B9%BF%E5%91%8A%E4%B8%AD%E7%9A%84%E9%93%BE%E6%8E%A5%E7%9A%84%E7%BD%91%E9%A1%B5%E8%AE%BF%E9%97%AE%E8%80%85%E6%89%80%E5%8D%A0%E7%9A%84%E7%99%BE%E5%88%86%E6%AF%94%E3%80%82%0A%23%23%23%23%20%5B%E6%9B%B4%E5%A4%9A%E7%BB%86%E8%8A%82%E5%85%B3%E4%BA%8E%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E6%9C%AF%E8%AF%AD%5D(https%3A%2F%2Fdevelopers.google.com%2Fmachine-learning%2Fglossary%2F)

转载于:https://www.cnblogs.com/pandaboy1123/p/10240316.html