What are the functions of the caret package?
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; choose the “optimal” model across these parameters; estimate model performance from a training set
How to tune a 5 model in caret package?
5Model Training and Tuning 5.1Model Training and Parameter Tuning 5.2An Example 5.3Basic Parameter Tuning 5.4Notes on Reproducibility 5.5Customizing the Tuning Process 5.5.1Pre-Processing Options 5.5.2Alternate Tuning Grids 5.5.3Plotting the Resampling Profile 5.5.4The trainControlFunction 5.5.5Alternate Performance Metrics
How to train glmnet final model Lambda in caret?
Take a look at glmnet vignette and how you can specify different lambda after training. and then walk through each of those sections, you can take a look at what train is doing. You can see in model$modelInfo$predict how it predicts on lambdaOpt and the rest of your sequence.
What’s the difference between Caret and cv.glmnet?
There seems to be a lot of confusion in the comparison of using glmnet within caret to search for an optimal lambda and using cv.glmnet to do the same task. Many questions were posed, e.g.:
How is predict.train used to extract predictions?
In the latter case, there are columns for each class. For predict.list, a list results. Each element is produced by predict.train . For extractPrediction, a data frame with columns: the names of the objects within models. If models is an un-named list, the values of object will be “Object1”, “Object2” and so on For extractProb, a data frame.
How is caret used in model building and evaluation?
caret has several functions that attempt to streamline the model building and evaluation process, as well as feature selection and other techniques. One of the primary tools in the package is the train function which can be used to
How to customize a model in caret to perform PLS?
The example was taken from this book and its package ChemometricsWithR. It highlighted some pitfalls when modeling using cross-validation techniques.
Do you need to create your own caret model?
However, you may want to create your own because: You can still get the benefits of the caret infrastructure by creating your own model.