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What do you need to know about the caret package?
trControl = trainControl (method = “cv”, number = 5) specifies that we will be using 5-fold cross-validation. method = glm specifies that we will fit a generalized linear model. The method essentially specifies both the model (and more specifically the function to fit said model in R) and package that will be used.
How is the base your function sample used in caret?
The base R function sample can be used to create a completely random sample of the data. The caret package has a function createDataPartition that conducts data splits within groups of the data. Later, once you have a set of predictions, various metrics can be used to evaluate performance.
How is caret used in predictive modeling and machine learning?
The process caret used was: Randomly sample the data with replacement. This means that a single observation could be chosen more than once. The total size of the modeling dataset will be the same as the original dataset but some sites will not be included and some will be included more than once.
How are factor variables handled in the caret package?
Different methods have different handling of categorical predictors. Some methods cannot handle factor variables. Thankfully, the R community has essentially provided a silver bullet for these issues, the caret package. Returning to the above list, we will see that a number of these tasks are directly addressed in the caret package.
How to calculate the dissimilarity of a sample in caret?
There are many methods in R to calculate dissimilarity. caret uses the proxy package. See the manual for that package for a list of available measures. Also, there are many ways to calculate which sample is “most dissimilar”.
How to plot data splitting in caret package?
In the plot below, rows in each panel correspond to different data splits (i.e. resamples) and the columns correspond to different data points. Also, red indicates samples that are in included in the training set and the blue indicates samples in the test set.
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