census dataset

Census Data Example from UC Irvine Machine Learning Repository

Includes a data frame of 1994 US census income from 48,842 people divided into a training set of 32,561 and an independent test set of 16,281. The training outcome variable y (yt for test) is binary and indicates whether or not a person’s income is greater than $50,000 per year. There are 12 predictor variables x (xt

for test) consisting of various demographic and financial properties associated with each person. It also included estimates of $Pr(y=1|x)$ obtained by several machine learning methods: gradient boosting on logistic scale using maximum likelihood (GBL), random forest (RF), and gradient boosting on the probability scale (GBP) using least–squares. data

Format

census

A list of 10 items.

• x: training data frame of 32561 observations on 12 predictor variables
• y: training binary response whether salary is above $50K or not
• xt: test data frame of 16281 observations predictor variables
• yt: test binary response whether salary is above $50K or not
• gbl: training GBL response variable
• gblt: test GBL response variable
• gbp: training GBP response variable
• gbpt: test GBP response variable
• rf: training RF response probabilities
• rft: test GBP response probabilities

Source

https://archive.ics.uci.edu/ml/datasets/census+income

census