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Applied Machine Learning for Tabular Data

@online{aml4td, author = {Kuhn, M and Johnson, K}, title = {{Applied Machine Learning for Tabular Data}}, year = {2023}, url = { https://aml4td.org}, urldate = {2024-05-06} } Applied Machine Learning for Tabular Data Preface Welcome! This is a work in progress. We want to create a practical guide to developing quality predictive models from tabular data.

The book takes a holistic view of the predictive modeling process and focuses on a few areas that are usually left out of similar works. Many readers found the Exercise sections of Applied Predictive Modeling to be helpful for solidifying the concepts presented in each chapter. aorsf(0.1.4) applicable(0.1.1) aspline(0.2.0) baguette(1.0.2) bestNormalize(1.9.1) bibtex(0.5.1) bonsai(0.2.1) broom(1.0.5) brulee(0.3.0) C50(0.1.8) Cubist(0.4.2.1) DALEXtra(2.3.0) dbarts(0.9-28) ddalpha(1.3.15) desirability2(0.0.1) dials(1.2.1) dimRed(0.2.6) discrim(1.0.1) doMC(1.3.8) dplyr(1.1.4) e1071(1.7-14) earth(5.3.3) embed(1.1.4) fastICA(1.2-4) finetune(1.2.0) GA(3.2.4) gganimate(1.0.9) ggforce(0.4.2) ggiraph(0.8.9) ggplot2(3.5.1) glmnet(4.1-8) gt(0.10.1) hardhat(1.3.1) ipred(0.9-14) irlba(2.3.5.1) kernlab(0.9-32) kknn(1.3.1) klaR(1.7-3) lightgbm(4.3.0) mda(0.5-4) mgcv(1.9-1) mixOmics(6.25.1) modeldata(1.3.0) modeldatatoo(0.3.0) pamr(1.56.2) parsnip(1.2.1) partykit(1.2-20) patchwork(1.2.0) plsmod(1.0.0) probably(1.0.3) pROC(1.18.5) purrr(1.0.2) ragg(1.3.1) ranger(0.16.0) recipes(1.0.10) rpart(4.1.23) rsample(1.2.1) RSpectra(0.16-1) rstudioapi(0.16.0) rules(1.0.2) shinylive(0.1.1) sparsediscrim(0.3.0) sparseLDA(0.1-9) spatialsample(0.5.1) splines2(0.5.1) stacks(1.0.4) stopwords(2.3) textrecipes(1.0.6.9000) themis(1.0.2) tidymodels(1.2.0) tidyposterior(1.0.1) tidyr(1.3.1) torch(0.12.0) tune(1.2.1) usethis(2.2.3) uwot(0.2.2) VBsparsePCA(0.1.0) viridis(0.6.5) workflows(1.1.4) workflowsets(1.1.0) xgboost(1.7.7.1) xrf(0.2.2) yardstick(1.3.1)

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