r-caret

问题 I've tried to use machine learning to make prediction based on time-series data. In one of the stackoverflow question (createTimeSlices function in CARET package in R) is an example of using createTimeSlices to cross-validation for model training and parameter tuning: library(caret) library(ggplot2) library(pls) data(economics) myTimeControl <- trainControl(method = "timeslice", initialWindow = 36, horizon = 12, fixedWindow = TRUE) plsFitTime <- train(unemploy ~ pce + pop + psavert, data =

问题 I'd like to build a logistic regression model using the caret package. This is my code. library(caret) df <- data.frame(response = sample(0:1, 200, replace=TRUE), predictor = rnorm(200,10,45)) outcomeName <-"response" predictors <- names(df)[!(names(df) %in% outcomeName)] index <- createDataPartition(df$response, p=0.75, list=FALSE) trainSet <- df[ index,] testSet <- df[-index,] model_glm <- train(trainSet[,outcomeName], trainSet[,predictors], method='glm', family="binomial", data = trainSet)

问题 I am wondering why predictions from 'Fold1' are actually predictions from the second fold in my predefined folds. I attach an example of what I mean. # load the library library(caret) # load the cars dataset data(cars) # define folds cv_folds <- createFolds(cars$Price, k = 5, list = TRUE, returnTrain = TRUE) # define training control train_control <- trainControl(method="cv", index = cv_folds, savePredictions = 'final') # fix the parameters of the algorithm # train the model model <- caret:

问题 I understand why parallel processing can be used during training only for XGB and cannot be used for other models. However, surprisingly I noticed that predict with xgb uses parallel processing too. I noticed this by accident when I split my large 10M + data frame into pieces to predict on using foreach %dopar% . This caused some errors so to try to get around them I switched to sequential looping with %do% but noticed in the terminal that all processors where being used. After some trial and

问题 I am trying to combine signals from different models using the example described here . I have different datasets which predicts the same output. However, when I combine the model output in caretList , and ensemble the signals, it gives an error Error in check_bestpreds_resamples(modelLibrary) : Component models do not have the same re-sampling strategies Here is the reproducible example library(caret) library(caretEnsemble) df1 <- data.frame(x1 = rnorm(200), x2 = rnorm(200), y = as.factor

问题 I am making a Shiny App in which, at the click of the actionButton, a model is trained using the caret package. As this training takes time - approximately 4-5 minutes - I wanted to display a progress bar which progresses as the model is trained. Thanks 回答1: To display progress bar in shiny app, you need to use withProgress function in server as below: withProgress(message = "Model is Training", value = 1.0, { ## Your code }) So, you put your code inside this function and it will display the

问题 When running rpart , I am getting an error message saying : Something is wrong; all the Accuracy metric values are missing: The dataset can be found here and has no NAs, can someone help? > rf.5.cv.1 # Random Forest # 891 samples # 6 predictor # 2 classes: '0', '1' # No pre-processing # Resampling: Cross-Validated (10 fold, repeated 10 times) # Summary of sample sizes: 802, 802, 803, 801, 801, 802, ... # Resampling results across tuning parameters: # mtry Accuracy Kappa # 2 0.8383655 0

问题 Relative newbie to predictive modeling--most of my training/experience is in inferential stats. I'm trying to predict student college graduation in 4 years. Basic issue is that I've done data cleaning (imputing, centering, scaling); split that processed/transformed data into training (70%) and testing (30%) sets; balanced the data using two approaches (because data was 65%=0, 35%=1--and I've found inconsistent advice on what classifies as unbalanced, but one source suggested anything not

问题 I want to create jack-knife data partitions for the data frame below, with the partitions to be used in caret::train (like the caret::groupKFold() produces). However, the catch is that I want to restrict the test points to say greater than 16 days, whilst using the remainder of these data as the training set. df <- data.frame(Effect = seq(from = 0.05, to = 1, by = 0.05), Time = seq(1:20)) The reason I want to do this is that I am only really interested in how well the model is predicting the

问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . I have used leaps package in R to perform forward and backward feature elimination. However, I want automate the cross validation and prediction operations. Therefore, how can I use forward/backward selection in caret? in leaps package you could do it this way forward <-