Problem: Comparison between several Machine Learning algorithms from the Weka library
A data frame with
A problem containing experimental data obtaining by comparing several instances of Machine Algorithms from the Weka library. The variables are as follows:
- method. Classification algorithms used in the experimen (NaiveBayes, J48, IBk)
- problem. Problems used as benchmark in the comparison, up to 12.
- featureSelection. Boolean parameter indicating if the data was preprocessed
- fold. For each configuration a 10-fold cross validation was performed. This variable is a numeric value ranging from 1 to 10.
- accuracy. This is a measure of the performance of each algorithm. Representing the percentage of correctly classified instances.
- trainingTime. A second measure of performance. This one indicates the time in seconds that took the algorithm to build the model.