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When to use a model in the RPART function?
If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data. keep a copy of the x matrix in the result. keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.
When to use EXP in recursive partitioning?
If y is a survival object, then method = “exp” is assumed, if y has 2 columns then method = “poisson” is assumed, if y is a factor then method = “class” is assumed, otherwise method = “anova” is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future.
Which is the vector of non negative costs in rpart?
See rpart.control. a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.
Which is the complexity parameter in rpart ( )?
This amount is specified by the complexity parameter, cp, in the call to rpart (). Setting cp to a negative amount ensures that the tree will be fully grown. This is not always a good idea since it will typically produce over-fitted trees, but trees can be pruned back as discussed later in this article.
How is rpart used to split the activity vector?
In the first dataset, we did not specify that Activity was an ordered factor, so rpart tested every possible way to split the levels of the Activity vector. In the second dataset, Activity was specified as an ordered factor so rpart only tested splits that separated the ordered set of Activity levels.
What is the overall measure of variable importance in rpart?
From the rpart documentation, “An overall measure of variable importance is the sum of the goodness of split measures for each split for which it was the primary variable…” When rpart grows a tree it performs 10-fold cross validation on the data.
How does rpart do cross validation on data?
When rpart grows a tree it performs 10-fold cross validation on the data. Use printcp () to see the cross validation results. The rel error of each iteration of the tree is the fraction of mislabeled elements in the iteration relative to the fraction of mislabeled elements in the root.
How to change the default in rpart gormanalysis?
To alter the default, equal penalization of mislabeled target classes set the loss component of the parms parameter to a matrix where the (i,j) element is the penalty for misclassifying an i as a j. (The loss matrix must have 0s in the diagonal). For example, consider the following training data.