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New guy ... help interpreting data
I need some help interpreting the output of auto-model. I have a true/false label with 600/11000 values and approximately 12,000 examples. At first glance, the random forest is more accurate, but then the AUC is much higher for the gradient Boosted Trees, and the precision points at decision trees and random forest. I am not an expert in statistics and I would much appreciate if someone can break this down for me and tell me if any of the predictions are statistically meaningful and how I go about determining that.
|Model||Accuracy (%)||Classification Error (%)||AUC||Precision (%)||Recall (%)||F-Measure (%)||Sensitivity %||Specificity (%)|
|Generalized Linear Model||94.8||5.2||0.855||40.0||13.1||19.8||13.1||99|
|Gradient Boosted Trees||94.6||5.4||0.915||40.6||21.3||28.0||21.3||98.4|