Example: using PCA to reduce the dimensionality of semiconductor

半导体是在一些极为先进的工厂中制造出来的。设备的生命早期有限，并且花费极其巨大。虽然通过早期测试和频繁测试来发现有瑕疵的产品，但仍有一些存在瑕疵的产品通过测试。如果我们通过机器学习技术用于发现瑕疵产品，那么它就会为制造商节省大量的资金。具体来讲，它拥有590个特征。我们看看能否对这些特征进行降维处理。对于数据的缺失值的问题，将缺失值NaN(Not a Number缩写)，全部用平均值来替代(如果用0来处理的策略就太差了)。
收集数据：提供文本文件,文件名：
secom.data.文本文件部分数据格式如下：

Semiconductors are manufactured in highly advanced fabrication facilities. The production equipment features a limited initial service life and incurs extremely high costs. Although defective products can be identified through initial and regular testing, some defective units still manage to pass the inspection. If machine learning techniques are adopted to detect such defective products, it will enable manufacturers to save a significant amount of capital. Specifically, this dataset contains 590 features, and we intend to investigate whether dimensionality reduction can be implemented on these features. Regarding the missing value problem in the dataset, all missing values marked as NaN (short for Not a Number) will be replaced with the mean value; using 0 for imputation is an inadequate strategy. Data collection: The raw data is provided in a text file named secom.data. The partial data format of this text file is as follows:

搜集汇总

背景与挑战

背景概述

该数据集名为“Example: using PCA to reduce the dimensionality of semiconductor”，是一个公共数据集，包含590个特征的半导体制造数据，用于通过PCA降维技术检测瑕疵产品以节省成本。数据文件为secom.data.txt，大小约5.14MB，上传于2019年10月24日，缺失值已用平均值处理。