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Low Recall High Accuracy
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
Weka Accuracy:76.7639 %
For Random Forrest:
Rapidminer Recall: 16.60% +/- 6.01% (micro average: 16.59%)
Weka Recall: 0.843
Weka Accuracy:84.2897 %
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
Weka Accuracy:82.4396 %
For Decision Tree
Weka 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.
Changing criterion,maximal dept, prunning,confidence,preprunning,minimal gain, leaf size,minimal size for split,number of preprunning alternatives
Changing number of trees, criterion,prunning,confidence,preprunning, random splits,guess subset ratio, voting strategy ets
But still recall value is low