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Як боротися з ієрархічними / вкладеними даними в машинному навчанні
Я поясню свою проблему на прикладі. Припустимо, ви хочете передбачити дохід фізичної особи за деякими ознаками: {Вік, стать, країна, регіон, місто}. У вас такий навчальний набір даних train <- data.frame(CountryID=c(1,1,1,1, 2,2,2,2, 3,3,3,3), RegionID=c(1,1,1,2, 3,3,4,4, 5,5,5,5), CityID=c(1,1,2,3, 4,5,6,6, 7,7,7,8), Age=c(23,48,62,63, 25,41,45,19, 37,41,31,50), Gender=factor(c("M","F","M","F", "M","F","M","F", "F","F","F","M")), Income=c(31,42,71,65, 50,51,101,38, 47,50,55,23)) train CountryID RegionID …
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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