import weka.core.Instances.*
filename = 'C:\Users\Girish\Documents\MATLAB\DRESDEN_NSC.csv';
loader = weka.core.converters.CSVLoader();
loader.setFile(java.io.File(filename));
data = loader.getDataSet();
data.setClassIndex(data.numAttributes()-1);
%% classification
classifier = weka.classifiers.trees.J48();
classifier.setOptions( weka.core.Utils.splitOptions('-C 0.25 -M 2') );
classifier.buildClassifier(data);
classifier.toString()
ev = weka.classifiers.Evaluation(data);
v(1) = java.lang.String('-t');
v(2) = java.lang.String(filename);
v(3) = java.lang.String('-split-percentage');
v(4) = java.lang.String('66');
prm = cat(1,v(1:4));
ev.evaluateModel(classifier, prm)
Result:
Time taken to build model: 0.04 seconds
Time taken to test model on training split: 0.01 seconds
=== Error on training split ===
Correctly Classified Instances 767 99.2238 %
Incorrectly Classified Instances 6 0.7762 %
Kappa statistic 0.9882
Mean absolute error 0.0087
Root mean squared error 0.0658
Relative absolute error 1.9717 %
Root relative squared error 14.042 %
Total Number of Instances 773
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.994 0.009 0.987 0.994 0.990 0.984 0.999 0.999 Nikon
1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 Sony
0.981 0.004 0.990 0.981 0.985 0.980 0.999 0.997 Canon
Weighted Avg. 0.992 0.004 0.992 0.992 0.992 0.988 1.000 0.999
=== Confusion Matrix ===
a b c <-- classified as
306 0 2 | a = Nikon
0 258 0 | b = Sony
4 0 203 | c = Canon
=== Error on test split ===
Correctly Classified Instances 358 89.9497 %
Incorrectly Classified Instances 40 10.0503 %
Kappa statistic 0.8482
Mean absolute error 0.0656
Root mean squared error 0.2464
Relative absolute error 14.8485 %
Root relative squared error 52.2626 %
Total Number of Instances 398
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.885 0.089 0.842 0.885 0.863 0.787 0.908 0.832 Nikon
0.993 0.000 1.000 0.993 0.997 0.995 0.997 0.996 Sony
0.796 0.060 0.841 0.796 0.818 0.749 0.897 0.744 Canon
Weighted Avg. 0.899 0.048 0.900 0.899 0.899 0.853 0.938 0.867
=== Confusion Matrix ===
a b c <-- classified as
123 0 16 | a = Nikon
0 145 1 | b = Sony
23 0 90 | c = Canon
import weka.core.Instances.*
filename = 'C:\Users\Girish\Documents\MATLAB\DRESDEN_NSC.csv';
loader = weka.core.converters.CSVLoader();
loader.setFile(java.io.File(filename));
data = loader.getDataSet();
data.setClassIndex(data.numAttributes()-1);
%% classification
classifier = weka.classifiers.trees.J48();
classifier.setOptions( weka.core.Utils.splitOptions('-C 0.1 -M 1') );
classifier.buildClassifier(data);
classifier.toString()
ev = weka.classifiers.Evaluation(data);
v(1) = java.lang.String('-t');
v(2) = java.lang.String(filename);
v(3) = java.lang.String('-split-percentage');
v(4) = java.lang.String('66');
prm = cat(1,v(1:4));
ev.evaluateModel(classifier, prm)
Result:
Time taken to build model: 0.04 seconds
Time taken to test model on training split: 0 seconds
=== Error on training split ===
Correctly Classified Instances 767 99.2238 %
Incorrectly Classified Instances 6 0.7762 %
Kappa statistic 0.9882
Mean absolute error 0.0087
Root mean squared error 0.0658
Relative absolute error 1.9717 %
Root relative squared error 14.042 %
Total Number of Instances 773
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.994 0.009 0.987 0.994 0.990 0.984 0.999 0.999 Nikon
1.000 0.000 1.000 1.000 1.000 1.000 1.000 1.000 Sony
0.981 0.004 0.990 0.981 0.985 0.980 0.999 0.997 Canon
Weighted Avg. 0.992 0.004 0.992 0.992 0.992 0.988 1.000 0.999
=== Confusion Matrix ===
a b c <-- classified as
306 0 2 | a = Nikon
0 258 0 | b = Sony
4 0 203 | c = Canon
=== Error on test split ===
Correctly Classified Instances 358 89.9497 %
Incorrectly Classified Instances 40 10.0503 %
Kappa statistic 0.8482
Mean absolute error 0.0656
Root mean squared error 0.2464
Relative absolute error 14.8485 %
Root relative squared error 52.2626 %
Total Number of Instances 398
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.885 0.089 0.842 0.885 0.863 0.787 0.908 0.832 Nikon
0.993 0.000 1.000 0.993 0.997 0.995 0.997 0.996 Sony
0.796 0.060 0.841 0.796 0.818 0.749 0.897 0.744 Canon
Weighted Avg. 0.899 0.048 0.900 0.899 0.899 0.853 0.938 0.867
=== Confusion Matrix ===
a b c <-- classified as
123 0 16 | a = Nikon
0 145 1 | b = Sony
23 0 90 | c = Canon
两个拆分选项的结果相同，这是默认选项的结果，即J48分类器的-C 0.25 -M 2
请帮忙!!!在这里坚持了很长时间。不同的意思，但没有任何作用对我来说