tidymodels

问题 I'm trying to do a PCR with tidymodels however i'm keep runing into this problem. I know there is a similar post but the solution over there, doesn't work form my case. My data library(AppliedPredictiveModeling) data(solubility) train = solTrainY %>% bind_cols(solTrainXtrans) %>% rename(solubility = ...1) My PCR analysis train %<>% mutate_all(., as.numeric) %>% glimpse() tidy_rec = recipe(solubility ~ ., data = train) %>% step_corr(all_predictors(), threshold = 0.9) %>% step_pca(all

问题 Could somebody show me how to generate permutation-based variable implots within the tidy modelling framework? Currently, I have this: library(tidymodels) # variable importance final_fit_train %>% pull_workflow_fit() %>% vip(geom = "point", aesthetics = list(color = cbPalette[4], fill = cbPalette[4])) + THEME + ggtitle("Elastic Net") which generates this: However, I would like to have something like this It's not clear to me how the rather new tidy modelling framework integrates with the

问题 Could somebody show me how to generate permutation-based variable implots within the tidy modelling framework? Currently, I have this: library(tidymodels) # variable importance final_fit_train %>% pull_workflow_fit() %>% vip(geom = "point", aesthetics = list(color = cbPalette[4], fill = cbPalette[4])) + THEME + ggtitle("Elastic Net") which generates this: However, I would like to have something like this It's not clear to me how the rather new tidy modelling framework integrates with the

问题 Overview I have produced four models using the tidymodels package with the data frame FID (see below): General Linear Model Bagged Tree Random Forest Boosted Trees The data frame contains three predictors: Year (numeric) Month (Factor) Days (numeric) The dependent variable is Frequency (numeric) I am following this tutorial:- https://smltar.com/mlregression.html#firstregressionevaluation Issue I would like to plot the quantitative estimates for how well my model performed and whether these

问题 I'm using tidymodels to fit a PLS model but I'm struggling to find the PLS variable importance scores or coefficients. This is what I've tried so far; the example data is from AppliedPredictiveModeling package. Modeling fitting data(ChemicalManufacturingProcess) split <- ChemicalManufacturingProcess %>% initial_split(prop = 0.7) train <- training(split) test <- testing(split) tidy_rec <- recipe(Yield ~ ., data = train) %>% step_knnimpute(all_predictors()) %>% step_BoxCox(all_predictors()) %>%

问题 Issue I am attempting to undertake an analysis using the Tidymodels Package in R . I am following this tutorial below regarding decision tree learning in R:- Tutorial https://bcullen.rbind.io/post/2020-06-02-tidymodels-decision-tree-learning-in-r/ I have a data frame called FID (see below) where the dependent variable is the frequency (numeric) , and the predictor variables are:- Year (numeric), Month (factor), Monsoon (factor), and Days (numeric). I believe I have successfully followed the

来源： https://stackoverflow.com/questions/62189885/what-is-the-difference-among-prep-bake-juice-in-the-r-package-recipes

来源： https://stackoverflow.com/questions/61606337/computing-importance-measure-using-vip-package-on-a-parsnip-model

问题 I am trying to retain an ID on the row when predicting using a Random Forest model to merge back on to the original dataframe. I am using step_naomit in the recipe that removes the rows with missing data when I bake the training data, but also removes the records with missing data on the testing data. Unfortunately, I don't have an ID to easily know which records were removed so I can accurately merge back on the predictions. I have tried to add an ID column to the original data, but bake