How is the train function used in caret?
The caret package has several functions that attempt to streamline the model building and evaluation process. The train function can be used to evaluate, using resampling, the effect of model tuning parameters on performance
Where does the weights parameter go in caret?
No matter what class weights I input, the classifiers invariably label 20% as class 1 and 80% as class 2. Looking into the code a little more, it doesn’t appear that the “weights” parameter from the “train” function ever gets passed through to the call to the ksvm package, which is what caret uses for SVM training.
How to train a model in caret package?
5.5.4The trainControlFunction 5.5.5Alternate Performance Metrics 5.6Choosing the Final Model 5.7Extracting Predictions and Class Probabilities 5.8Exploring and Comparing Resampling Distributions
How to handle class imbalance with Caret and R?
Incorporating weights into the model can be handled by using the weights argument in the function. Note that the same seeds were used for each model to ensure that results from the same cross-validation folds are being used. Also keep in mind that for sampling methods, it is vital that you only sample the training set and not the test set as well.
Which is the resampling technique used in caret?
The resampling technique used for evaluating the performance of the model using a set of parameters in Caret by default is bootstrap, but it provides alternatives for using k-fold, repeated k-fold as well as Leave-one-out cross validation (LOOCV) which can be specified using trainControl ().
How to scale and center data in caret?
Also, we’ll scale and center the numerical data by using the convenient preprocess () in Caret. It is also very easy to use one hot encoding in Caret to create dummy variables for each level of a categorical variable. But first, we’ll convert the dependent variable to numerical.