Vou explicar meu problema com um exemplo. Suponha que você queira prever a renda de um indivíduo, com alguns atributos: {Idade, Sexo, País, Região, Cidade}. Você tem um conjunto de dados de treinamento como esse train <- data.frame(CountryID=c(1,1,1,1, 2,2,2,2, 3,3,3,3), RegionID=c(1,1,1,2,...
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Como lidar com dados hierárquicos / aninhados no aprendizado de máquina
regression
machine-learning
multilevel-analysis
correlation
dataset
spatial
paired-comparisons
cross-correlation
clustering
aic
bic
dependent-variable
k-means
mean
standard-error
measurement-error
errors-in-variables
regression
multiple-regression
pca
linear-model
dimensionality-reduction
machine-learning
neural-networks
deep-learning
conv-neural-network
computer-vision
clustering
spss
r
weighted-data
wilcoxon-signed-rank
bayesian
hierarchical-bayesian
bugs
stan
distributions
categorical-data
variance
ecology
r
survival
regression
r-squared
descriptive-statistics
cross-section
maximum-likelihood
factor-analysis
likert
r
multiple-imputation
propensity-scores
distributions
t-test
logit
probit
z-test
confidence-interval
poisson-distribution
deep-learning
conv-neural-network
residual-networks
r
survey
wilcoxon-mann-whitney
ranking
kruskal-wallis
bias
loss-functions
frequentist
decision-theory
risk
machine-learning
distributions
normal-distribution
multivariate-analysis
inference
dataset
factor-analysis
survey
multilevel-analysis
clinical-trials