cartdecision-theorymodelingr
So I got my training set with 70% of my data called "train" / 30% "test"
I use ctree to get my decision tree model with something like this code below :
model_ctree <- ctree(response ~ x1 + .. xn , data = train)
How can I apply this model to "test" and evaluate the model, use something like lift or gain chart or ROC; something that I would normally get from SAS miner?
I am new to R.
This is an interesting and very frequent problem in classification - not just in decision trees but in virtually all classification algorithms.
As you found empirically, a training set consisting of different numbers of representatives from either class may result in a classifier that is biased towards the majority class. When applied to a test set that is similarly imbalanced, this classifier yields an optimistic accuracy estimate. In an extreme case, the classifier might assign every single test case to the majority class, thereby achieving an accuracy equal to the proportion of test cases belonging to the majority class. This is a well-known phenomenon in binary classification (and it extends naturally to multi-class settings).
This is an important issue, because an imbalanced dataset may lead to inflated performance estimates. This in turn may lead to false conclusions about the significance with which the algorithm has performed better than chance.
The machine-learning literature on this topic has essentially developed three solution strategies.
You can restore balance on the training set by undersampling the large class or by oversampling the small class, to prevent bias from arising in the first place.
Alternatively, you can modify the costs of misclassification, as noted in a previous response, again to prevent bias.
An additional safeguard is to replace the accuracy by the so-called balanced accuracy. It is defined as the arithmetic mean of the class-specific accuracies, $\phi := \frac{1}{2}\left(\pi^+ + \pi^-\right),$ where $\pi^+$ and $\pi^-$ represent the accuracy obtained on positive and negative examples, respectively. If the classifier performs equally well on either class, this term reduces to the conventional accuracy (i.e., the number of correct predictions divided by the total number of predictions). In contrast, if the conventional accuracy is above chance only because the classifier takes advantage of an imbalanced test set, then the balanced accuracy, as appropriate, will drop to chance (see sketch below).
I would recommend to consider at least two of the above approaches in conjunction. For example, you could oversample your minority class to prevent your classifier from acquiring a bias in favour the majority class. Following this, when evaluating the performance of your classifier, you could replace the accuracy by the balanced accuracy. The two approaches are complementary. When applied together, they should help you both prevent your original problem and avoid false conclusions following from it.
I would be happy to post some additional references to the literature if you would like to follow up on this.
Old thread, but I think the "problem" is that your tree is very good, so pruning has no effect. Pruning means to eliminate leaves that are not increasing the accuracy significantly, i.e. to prevent overfitting. In your plotcp you are plotting the out-of-sample error of the model, computed by rpart through cross-validation: pruning simply removes leaves until you reach the minimum in that plot. The minimum is at cp=0, so there is nothing to prune.
Best Answer
Try this for class predictions:
Try this for class probabilities:
Try this for a roc curve:
Try this for a lift curve:
Sensitivity/specificity curve and precision/recall curve:
More info:
Also, you should check out the
caretpackage if you're building predictive models in R. It implements a number of out-of-sample evaluation schemes, including bootstrap sampling, cross-validation, and multiple train/test splits.caretis really nice because it provides a unified interface to all the models, so you don't have to remember, e.g., thattreeresponseis the function to get class probabilities from a ctree model. Here's an example of using 10-fold cross-validation to evaluation your model, which is much better than a single train/test split: