端到端的机器学习项目

最新推荐文章于 2022-07-04 22:11:46 发布

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最新推荐文章于 2022-07-04 22:11:46 发布 · 499 阅读

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#python #data-mining

本文详细介绍了从头到尾的机器学习项目实施步骤，包括问题定义、数据获取、数据探索、数据预处理、模型选择、系统优化、解决方案呈现和上线维护。强调了每个阶段的重点任务，如数据清洗、特征工程和模型调优，旨在实现业务目标。

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端到端的机器学习项目

主要步骤：

Frame the problem and look at the big picture

Get the data

Explore the data to gain insight

Prepare the data to better expose the underlying data patterns to Machine Learning algorithms

Explore many different models and short-list the best ones

Fine-tune your models and combine them into a great solution

Present your solution

Launch, monitor, and maintarin your system

1. Frame the Problem and Look at the Big Picture

Define the object in bussiness terms

How will your solution be used?

What are the current solutions/workarounds(if any?)

How should you frame this problem (supervised/unsupervised, online/offline, etc.)?

How should performance be measured?

Is the performance measure alighed with bussiness objective?

What would be the minimum performance needed to reach the bussiness objective?

What are comparable problem? Can you reuse experience or tools?

Is human expertise available?

How would you solve the problem manually?

List the assumptions you (or others) have made so far?

Verify assumptions if possible

2. Get the Data

Note: automate as much as possible so you can easily get flesh data.

List the data you need and how much you need

Find and document where you can get that data

Check how much space it will take

Check legal obligations, and get authorization if