Data mining - error values interpretation
I am wondering how error measures in Weka is interpreted. I understand how to interpret confusion matrix, kappa statics, ROC curve, and confusion matrix, but I cannot quite put the error measures into real life example. I have a very vauge idea that error measures tell the magnitude of the error of prediction somewhat, but how exactly? Could you please explain how error measures can fit in with real-life concrete example?
Penalty for unbalanced data in libsvm
What classifier with Weka?
Classification with fuzzy logic
PSNR-based classification & subimage-based classification
Any reason why these instance could be misclassified?
Calculating the area under curve from classification accuracy
Weka LibSVM one class classifier always predicts one class
Can tfidf be weighed to improve classification of sparse data in a corpus?
Fusion Classifier in Weka?
Using discretize filter in weka explorer
Multiclass classification and unbalanced dataset
How to perform nominal to numeric conversion of attributes in WEKA?
Ideas to determine cutoff points for cancer classification?
Discriminant Analysis Package for WEKA
How to get the probabilities of each class for the test instances in weka
rapidminer if else statement for attribute generation