Questions marquées «paired-comparisons»

29
Comment gérer les données hiérarchiques / imbriquées dans l'apprentissage automatique

Je vais expliquer mon problème avec un exemple. Supposons que vous souhaitiez prédire le revenu d'un individu en fonction de certains attributs: {âge, sexe, pays, région, ville}. Vous avez un ensemble de données de formation comme ça train <- data.frame(CountryID=c(1,1,1,1, 2,2,2,2, 3,3,3,3),...

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
28
Calcul de la répétabilité des effets d'un modèle lmer

Je viens de tomber sur cet article , qui décrit comment calculer la répétabilité (aka fiabilité, aka corrélation intraclasse) d'une mesure via la modélisation d'effets mixtes. Le code R serait: #fit the model fit = lmer(dv~(1|unit),data=my_data) #obtain the variance estimates vc = VarCorr(fit)...

mixed-model reliability intraclass-correlation repeatability spss factor-analysis survey modeling cross-validation error curve-fitting mediation correlation clustering sampling machine-learning probability classification metric r project-management optimization svm python dataset quality-control checking clustering distributions anova factor-analysis exponential poisson-distribution generalized-linear-model deviance machine-learning k-nearest-neighbour r hypothesis-testing t-test r variance levenes-test bayesian software bayesian-network regression repeated-measures least-squares change-scores variance chi-squared variance nonlinear-regression regression-coefficients multiple-comparisons p-value r statistical-significance excel sampling sample r distributions interpretation goodness-of-fit normality-assumption probability self-study distributions references theory time-series clustering econometrics binomial hypothesis-testing variance t-test paired-comparisons statistical-significance ab-test r references hypothesis-testing t-test normality-assumption wilcoxon-mann-whitney central-limit-theorem t-test data-visualization interactive-visualization goodness-of-fit
15
La précision de la machine augmentant le gradient diminue à mesure que le nombre d'itérations augmente

J'expérimente l'algorithme de la machine de renforcement de gradient via le caretpackage en R. À l'aide d'un petit ensemble de données d'admission à l'université, j'ai exécuté le code suivant: library(caret) ### Load admissions dataset. ### mydata <-

machine-learning caret boosting gbm hypothesis-testing t-test panel-data psychometrics intraclass-correlation generalized-linear-model categorical-data binomial model intercept causality cross-correlation distributions ranks p-value z-test sign-test time-series references terminology cross-correlation definition probability distributions beta-distribution inverse-gamma missing-data paired-comparisons paired-data clustered-standard-errors cluster-sample time-series arima logistic binary-data odds-ratio medicine hypothesis-testing wilcoxon-mann-whitney unsupervised-learning hierarchical-clustering neural-networks train clustering k-means regression ordinal-data change-scores machine-learning experiment-design roc precision-recall auc stata multilevel-analysis regression fitting nonlinear jmp r data-visualization gam gamm4 r lme4-nlme many-categories regression causality instrumental-variables endogeneity controlling-for-a-variable
8
Comment effectuer une SVD pour imputer des valeurs manquantes, un exemple concret

J'ai lu les excellents commentaires sur la façon de traiter les valeurs manquantes avant d'appliquer SVD, mais j'aimerais savoir comment cela fonctionne avec un exemple simple: Movie1 Movie2 Movie3 User1 5 4 User2 2 5 5 User3 3 4 User4 1 5 User5 5 1 5 Étant donné la matrice ci-dessus, si je...

r missing-data data-imputation svd sampling matlab mcmc importance-sampling predictive-models prediction algorithms graphical-model graph-theory r regression regression-coefficients r-squared r regression modeling confounding residuals fitting glmm zero-inflation overdispersion optimization curve-fitting regression time-series order-statistics bayesian prior uninformative-prior probability discrete-data kolmogorov-smirnov r data-visualization histogram dimensionality-reduction classification clustering accuracy semi-supervised labeling state-space-models t-test biostatistics paired-comparisons paired-data bioinformatics regression logistic multiple-regression mixed-model random-effects-model neural-networks error-propagation numerical-integration time-series missing-data data-imputation probability self-study combinatorics survival cox-model statistical-significance wilcoxon-mann-whitney hypothesis-testing distributions normal-distribution variance t-distribution probability simulation random-walk diffusion hypothesis-testing z-test hypothesis-testing data-transformation lognormal r regression agreement-statistics classification svm mixed-model non-independent observational-study goodness-of-fit residuals confirmatory-factor neural-networks deep-learning