Below example results for same dataset. And dataset has not missing value;
For Naive Bayes:Rapidminer Recall: 26.35% +/- 5.17% (micro average: 26.37%)
Weka Recall: 0.768
Rapidminer Precision: 43.41%
Weka Precision: 0.735
Rapidminer Accuracy:77.14
Weka Accuracy:76.7639 %
For Random Forrest: Rapidminer Recall: 16.60% +/- 6.01% (micro average: 16.59%)
Weka Recall: 0.843
Rapidminer Accuracy:81.75%
Weka Accuracy:84.2897 %
For KNN: Rapidminer Recall: 12.89% +/- 3.82% (micro average: 12.89%)
Weka Recall: 0.824
Rapidminer Precision: 55.82% +/- 12.05% (micro average: 55.77%)
Weka Precision: 0.810
Rapidminer Accuracy:79.40%
Weka Accuracy:82.4396 %
For Decision TreeWeka Accuracy; 81.4989 %
RapidMiner Accuracy: 83.07%
Weka Recall; 0.815
RapidMiner Recall: 30.67%
Why rapidminer recall and and precision value is very low despite accuracy is high. Especially recall value. ?
My process is in attach. I use same process for other algorithms
**Also I try other settings in related Algorithms for improve recall in Rapidminer.
I mean ,
For Example KNN;Changing K values, measure types, mixes measure, weighted vote.
Decision Tress;Changing criterion,maximal dept, prunning,confidence,preprunning,minimal gain, leaf size,minimal size for split,number of preprunning alternatives
Random Forrest;
Changing number of trees, criterion,prunning,confidence,preprunning, random splits,guess subset ratio, voting strategy ets
But still recall value is low