목록Applied/Statistical Learning (12)
ISL sol https://blog.princehonest.com/stat-learning/ https://blog.princehonest.com/stat-learning/ blog.princehonest.com https://www.kaggle.com/lmorgan95/code Liam Morgan | Discussion Contributor 28 year old Data Scientist working at Creditspring in the UK where I spend most of my time building predictive models. Currently inactive on Kaggle but hoping to return some day when I get some inspirati..
2. Linear Regression
#Comment ## Notation # Explanatory variables : X = t(x1,x2,...,xp) # Real-valued response : Y # Training data : D = {(xi,yi)|i=1,..n} # General model : Yi = f(xi)+입실론i, e~N(0,sigma^2) # Estimate the regression function : f(x).hat = E(Y|X) # 즉 X가 주어졌을 때 y의 기대값으로 function을 추정하는게 핵심 ##Variable # y가 가질 수 있는 값을 실수 -oo ~ oo까지 # x가 가질 수 있는 값은 # 1 quantitive input(X), 2 categorical(dummy codig을 통해) # 3 ..
1. Data Exploration
# 0. Install "ISLR2" package install.packages("ISLR2") library(ISLR2) # 1. Preparation of "Auto" Dataset #dimension 392 * 9 dim(Auto) View(Auto) head(Auto) #mission value 확인 sum(is.na(Auto)) #na.omit로 missing value 제거하면 dimension 392 * 9 Auto