#linear discriminant analysis

original source : StatQuest: Linear Discriminant Analysis (LDA) clearly explained.

https://youtu.be/azXCzI57Yfc

바로위 그림은 PCA를 이용한 경우를 보여주고 있다.

바로위 그림은 PCA를 이용한 경우를 보여주고 있다.

아래그림이 LDA를 이용한 경우를 보여주고 있다.

아래 부분은 위에서 설명한 2개가 아닌 3개의 클래스로 구분하는 과정을 설명하고 있다.

윗 부분은 위에서 설명한 2개가 아닌 3개의 클래스로 구분하는 과정을 설명하고 있다.

Dimensionality Reduction via Linear Discriminant Analysis

In my previous post, I had written about principal component analysis (PCA) for dimensionality reduction. In PCA, the class label of each and every example, even if available, is ignored and only the feature values from each example are considered. Thus, PCA is considered an unsupervisedapproach.  The emphasis in PCA is to preserve data variability as much as possible while reducing the…

The goal of this analysis is to explore the relationship between “binge” drinking and other factors in order to create a model to predict recent if a person has recently participated in binge drinking. The model that was developed is based on a large dataset of survey responses from individuals. Those individuals not only answered questions about their recent alcohol use but many others related to legal / illegal drug use and other demographic questions. The result of this analysis is then compared to the research of others on this subject. The results of the analysis show that a reasonably accurate model can be built to predict alcohol “binge” drinking of an individual within the last thirty days. One of the surprising indicators if a person was likely to abuse alcohol was if that person was a smoker or not.