The metric "ROC" was not in the result set.
Train caret code#
The second way is to use the levels function, by which you will have explicit control over the names themselves showing it here again with names X0 and X1 levels(df$Y) <- c("X0", "X1")Īfter adding either one of the above lines, the shown train() code will run smoothly (replacing training with df), but it will still not produce any ROC values, giving instead the warning: Warning messages:ġ: In fault(x, y, weights = w. You can do this by at least two ways, after you have built your dataframe df the first one is hinted at the error message, i.e. The first is the error message, which says it all: you have to use something else than "0", "1" as values for your dependent factor variable Y. However, the "ROC" metric is my metric of comparison for the best model in my case, so I'm trying to make a model with "ROC" metric.ĮDIT: Code example: # You have to run all of this BEFORE running the modelĭf <- cbind(classes, floats, dummy, chr, Y) PS: The code works perfectly if you erase classProbs=TRUE in trainControl() and metric="ROC" in train(). The dependent variable Y is already a factor, but is not a valid R variable name?. I don't understand what means "use factor levels that can be used as valid R variable names". That can be used as valid R variable names (see ?make.names for help). Variables names will be converted to X0, X1. This will cause errors when class probabilities are generated because the However, it gives me this error: Error: At least one of the class levels is not a valid R variable name Print(cmnn) # The confusion matrix is to assess/compare the model Nnprediction <- predict(model_nn, testing, type="prob")Ĭmnn <-confusionMatrix(nnprediction,testing$Y) I'm trying to predict the probability with this code (neural networks, caret package): library(caret) The dependent variable Y is binary (factor) with values 0 and 1. the train dataframe is splitted in training and testing dataframes. Role of "case weight" for a single variable.The df is splitted in the train and test dataframes. Note that case weights can be passed into train using a More details on using recipes can be found at More details on this function can be found atĪ variety of models are currently available and are enumeratedīy tag (i.e. When using string kernels, the vector ofĬharacter strings should be converted to a matrix with a single The function was designed to work with simple matricesĪnd data frame inputs, so some functionality may not work (e.g. The underlying model fit function can deal with the objectĬlass. The predictors in x can be most any object as long as The entire training set is used to fit a final model. Optimal resampling statistic is chosen as the final model and
Samples is calculated and the mean and standard deviation is Across each data set, the performance of held-out Slightly different data for each candidate combination of tuning Parameters (if any) is created and the model is trained on Train can be used to tune models by picking theĬomplexity parameters that are associated with the optimal Predict new samples (see trainControl) Details Model fit and, optionally, prediction for the time to The entire call to train, final for the final timesĪ list of execution times: everything is for Performance metrics that are produced by the summary function maximizeĪ logical recycled from the function arguments.
TrainControl controls how much of the resampled If leave-one-outĬross-validation or out-of-bag estimation methods are requested, preProcessĪ data frame with columns for each performance Summary metric will be used to select the optimal model. The (matched) function call with dots expanded dotsĪ list containing any. bestTuneĪ data frame with the final parameters. Training error rate and values of the tuning parameters. ValueĪ list is returned of class train containing: method The factors appearing as variables in the model formula. A list of contrasts to be used for some or all
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