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What is AUC in decision tree?
AUC means Area Under Curve ; you can calculate the area under various curves though. Common is the ROC curve which is about the tradeoff between true positives and false positives at different thresholds. This AUC value can be used as an evaluation metric, especially when there is imbalanced classes.
How do you evaluate a decision tree classifier?
Evaluating Decision Trees In a binary classifier, one great metric to use is the ROC-AUC curve and a confusion matrix. These metrics will require the following imports. We then want to define some functions to get the AUC scores and plot the ROC-AUC curve. clf stands for classifier model.
How to calculate and use the AUC score?
The AUC score is simply the area under the curve which can be calculated with Simpson’s Rule. The bigger the AUC score the better our classifier is. Given two classifiers A & B, we expect two different ROC curves. Consider the plot below:
How to plot AUC curve in Python for decision tree classifier?
How to plot AUC curve in python for decision tree classifier in python? AUC means Area Under Curve ; you can calculate the area under various curves though. Common is the ROC curve which is about the tradeoff between true positives and false positives at different thresholds.
Which is the best way to use a decision tree?
The most used way for decision trees is to use the proportion of classes from the leaf nodes. So, for example you have built a decision tree and when you predict the class for an instance you arrive at a leaf node which have (stored from the learning phase) 10 instances of class c1 and 15 instances of class c2, you can use the ratios as the scores.
Common is the ROC curve which is about the tradeoff between true positives and false positives at different thresholds. This AUC value can be used as an evaluation metric, especially when there is imbalanced classes. So if i may be a geek, you can plot the ROC curve and then calculate the AUC 😉