Contents
How does the loss parameter work in rpart?
Trying to understand how the loss-parameter works in the rpart package in R Looking at this example: https://www.listendata.com/2015/04/ways-to-correct-class-imbalances.html it tells me that the cost matrix should be entered with the highest misclassification cost in the cell that is the least desired prediction.
How to create a confusion matrix in rpart?
The predict function for an rpart model with type=”class” will return the predicted class for each observation. Lastly, you can build the confusion matrix by running table between the prediction and true outcome:
Do you change the loss matrix after growing the tree?
I don’t see why you would change the loss matrix after growing the tree. The loss matrix is something you decide on a-priori, you don’t change it after the analysis. With the loss matrix you mention, the cost of a false negative is 4 times as high as the cost of a false positive (or the other way around).
When to use loss matrix in decision tree?
Both models give pretty high accuracy of 70+% under confusion matrix. Yes, the loss matrix should be used when growing the tree (why else would one define a custom loss matrix?). Different loss matrices likely (but not necessarily) yield different splits.
How to plot MyTree using rpart-gormanalysis?
We can plot mytree by loading the rattle package (and some helper packages) and using the fancyRpartPlot () function. The decision tree correctly identified that if a claim involved a rear-end collision, the claim was most likely fraudulent. By default, rpart uses gini impurity to select splits when performing classification.
How does rpart keep track of a decision tree?
If we try to build a decision tree on this data.. Once again we’re left with just a root node. Internally, rpart keeps track of something called the complexity of a tree. The complexity measure is a combination of the size of a tree and the ability of the tree to separate the classes of the target variable.
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.
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.