---
title: Minitab博客 | Machine Learning
description: Machine Learning |
---

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# Minitab博客 | Machine Learning

做出最佳数据驱动决策的技巧和技术

### 热门文章

### 编辑推荐

### 热门文章 - Machine Learning

[通过机器学习和 R 集成减少过程缺陷的 4 个步骤 4 Steps to Reduce Process Defects with Machine Learning and R Integration](https://blog.minitab.com/zh/machine-learning-r-integration)

[未来就在眼前：利用预测分析改进供应链 The Future is Now: Improving the Supply Chain with Predictive Analytics](https://blog.minitab.com/zh/the-future-is-now-improving-the-supply-chain-with-predictive-analytics-0)

[有兴趣了解文本挖掘？利用 Minitab 中的全新 Python 集成开启探索之旅！| Interested in Text Mining? Get Started in Minitab with New Python Integration!](https://blog.minitab.com/zh/text-mining-in-minitab-with-python-integration)

[修剪决策树，造出好纸张：Minitab 中的预测分析和根本原因分析 | Trimming Decision Trees to Make Paper: Predictive Analytics and Root Cause Analysis in Minitab](https://blog.minitab.com/zh/trimming-decision-trees-paper-predictive-analytics-root-cause-analysis)

[揭开机器学习中功能工程设计的神秘面纱 | Demystifying Feature Engineering for Machine Learning](https://blog.minitab.com/zh/demystifying-feature-engineering-for-machine-learning)

<https://blog.minitab.com/zh/machine-learning-r-integration>

## [通过机器学习和 R 集成减少过程缺陷的 4 个步骤 4 Steps to Reduce Process Defects with Machine Learning and R Integration](https://blog.minitab.com/zh/machine-learning-r-integration)

[Machine Learning](https://blog.minitab.com/zh/tag/machine-learning)  | 8 分钟阅读

We explore how to build a Neural Network in Minitab using R to compliment and compare with results from our own Predictive Analytics module.

[立即阅读！](https://blog.minitab.com/zh/machine-learning-r-integration)

<https://blog.minitab.com/zh/the-future-is-now-improving-the-supply-chain-with-predictive-analytics-0>

## [未来就在眼前：利用预测分析改进供应链 The Future is Now: Improving the Supply Chain with Predictive Analytics](https://blog.minitab.com/zh/the-future-is-now-improving-the-supply-chain-with-predictive-analytics-0)

[Machine Learning](https://blog.minitab.com/zh/tag/machine-learning)  | 13 分钟阅读

See how predictive analytics can be used in the supply chain to optimize inventory, route planning, risk management, customer satisfaction and more. 供应链预测分析是利用数据挖掘、机器学习和统计分析来识别供应链数据的模式和趋势，并对未来的业绩和结果进行预测。 供应链预测分析的目标是通过更准确地了解未来的需求、供应和其他可能影响供应链的关键因素来改进决策和战略规划。这使企业能够积极管理和优化其供应链运营、降低成本、提高效率，进而提高客户满意度

[立即阅读！](https://blog.minitab.com/zh/the-future-is-now-improving-the-supply-chain-with-predictive-analytics-0)

<https://blog.minitab.com/zh/text-mining-in-minitab-with-python-integration>

## [有兴趣了解文本挖掘？利用 Minitab 中的全新 Python 集成开启探索之旅！| Interested in Text Mining? Get Started in Minitab with New Python Integration!](https://blog.minitab.com/zh/text-mining-in-minitab-with-python-integration)

[Machine Learning](https://blog.minitab.com/zh/tag/machine-learning)  | 10 分钟阅读

现如今，内容无处不在，随时可供访问！尼尔森 (Nielsen) 的一项研究发现，美国成人每天用于阅读、聆听、观看媒体以及与媒体互动的时间超过 11 小时。当下大家宅在家中，想必这个数值只会更高。可用内容层出不穷，您或许会想知道：是否存在一种定量方式，让我们能够深入了解可用文本？

[立即阅读！](https://blog.minitab.com/zh/text-mining-in-minitab-with-python-integration)

<https://blog.minitab.com/zh/trimming-decision-trees-paper-predictive-analytics-root-cause-analysis>

## [修剪决策树，造出好纸张：Minitab 中的预测分析和根本原因分析 | Trimming Decision Trees to Make Paper: Predictive Analytics and Root Cause Analysis in Minitab](https://blog.minitab.com/zh/trimming-decision-trees-paper-predictive-analytics-root-cause-analysis)

[Lean Six Sigma](https://blog.minitab.com/zh/tag/lean-six-sigma-2)  | 10 分钟阅读

我们从过程中收集的观测数据越来越多，因此，我们可能需要新工具才能够获得有意义的见解。您可以添加现代机器学习技术和传统统计工具来分析、改进及掌控各个过程。我们查看一个示例，该示例从二元逻辑回归开始，并以分类和回归树 (CART®) 结束。

[立即阅读！](https://blog.minitab.com/zh/trimming-decision-trees-paper-predictive-analytics-root-cause-analysis)

<https://blog.minitab.com/zh/demystifying-feature-engineering-for-machine-learning>

## [揭开机器学习中功能工程设计的神秘面纱 | Demystifying Feature Engineering for Machine Learning](https://blog.minitab.com/zh/demystifying-feature-engineering-for-machine-learning)

[Machine Learning](https://blog.minitab.com/zh/tag/machine-learning)  | 6 分钟阅读

https://info.minitab.com/resources/webinars/unleashing-the-power-of-python?utm\_id=PROMO%3A62074B0EB75DD%3A478&utm\_campaign=APAC\_Chinese\_Blog&utm\_medium=Referral&utm\_source=Hubspot&utm\_content=%E6%9C%89%E5%85%B4%E8%B6%A3%E4%BA%86%E8%A7%A3%E6%96%87%E6%9C%AC%E6%8C%96%E6%8E%98%EF%BC%9F%E5%88%A9%E7%94%A8+Minitab+%E4%B8%AD%E7%9A%84%E5%85%A8%E6%96%B0+Python+%E9%9B%86%E6%88%90%E5%BC%80%E5%90%AF%E6%8E%A2%E7%B4%A2%E4%B9%8B%E6%97%85%EF%BC%81%7C+Interested+in+Text+Mining%3F+Get+Started+in+Minitab+with+New+Python+Integration%21

[立即阅读！](https://blog.minitab.com/zh/demystifying-feature-engineering-for-machine-learning)

<https://blog.minitab.com/zh/branching-out-using-cart-for-survey-data>

## [另辟蹊径：使用 CART 作为分析分类调查数据的替代方法 | Branching Out: Using CART For Alternative Ways to Analyze Categorical Survey Data](https://blog.minitab.com/zh/branching-out-using-cart-for-survey-data)

[Machine Learning](https://blog.minitab.com/zh/tag/machine-learning)  | 12 分钟阅读

尝试了解客户/患者行为是一件很有挑战的事。研究人员往往会使用调查数据并频繁使用 Minitab Statistical Software 中的回归功能对其进行分析。但是，若结果变量是分类数据，而不是定量数据，则无法非常直接或直观地解释逻辑回归的结果。

[立即阅读！](https://blog.minitab.com/zh/branching-out-using-cart-for-survey-data)

<https://blog.minitab.com/zh/file-not-found-missing-output-quick-tips-to-troubleshoot-python-errors>

## [找不到文件？输出缺失？Python 错误疑难解答小提示 | File Not Found? Missing Output? Quick Tips to Troubleshoot Common Errors](https://blog.minitab.com/zh/file-not-found-missing-output-quick-tips-to-troubleshoot-python-errors)

[Machine Learning](https://blog.minitab.com/zh/tag/machine-learning)  | 9 分钟阅读

Minitab 的 Python 连接功能支持用户在 Minitab 界面中运行开源 Python 脚本并在 Minitab 输出窗格中显示结果。借助与 Python 的连接，您仅需单击几下就可享用 Minitab 菜单中尚未提供的自定义分析或图形。最便利的是，您无需精通 Python 编程即可完成设置。

[立即阅读！](https://blog.minitab.com/zh/file-not-found-missing-output-quick-tips-to-troubleshoot-python-errors)

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