Machine Learning Decision Tree

Machine Learning Decision tree (for both discrete and continuous classification problems). Examples use the optional AJE.AccordHelper.dll .NET component library to assist in calling the Accord Machine Learning libraries from Python.

Decision Tree
Root
↳
X <= -1.03472995730602
↳
1
↳
X > -1.03472995730602
↳
Y <= -0.819412029522614
↳
1
↳
Y > -0.819412029522614
↳
X <= 0.0487916348028351
↳
Y <= 0.169169245923048
↳
0
↳
Y > 0.169169245923048
↳
Y <= 1.11251655730791
↳
1
↳
Y > 1.11251655730791
↳
0
↳
X > 0.0487916348028351
↳
0

Model Testing


Confusion Matrix
True
Positives		 True
Negatives		 False
Positives		 False
Negatives		 Sensitivity Specificity Efficiency Hits Accuracy
50 50 0 0 1 1 1 100 1


Data Inputs

Test Data inputs showing the first 25 rows only.
X Y G
-0.876847427782256 1.99631882419913 0
-0.748759324869685 1.99724851416656 0
-0.63557469450121 1.97804657885838 0
-0.513769071062192 1.97322477728966 0
-0.382577547284093 1.95507722390739 0
-0.275144211134104 1.9238137888256 0
-0.156802752494605 1.94921969451005 0
-0.0460020585481831 1.89534254245404 0
0.0841522573629316 1.87310408178925 0
0.192063131363708 1.86815753225309 0
0.238547032360424 1.81166416536336 0
0.381412694469276 1.83086992461685 0
0.431182118837231 1.75531247939342 0
0.562589082378018 1.72544480585522 0
0.553294268828727 1.68904788552912 0
0.730976261421586 1.61052206353837 0
0.722164981441283 1.63311295208607 0
0.861069302278025 1.56245019650894 0
0.82510794508344 1.43584622512398 0
0.82526113243736 1.45639119641546 0
0.94872162601077 1.39336755213731 0
1.0017052775839 1.27576844658262 0
0.966788666823623 1.32137523307275 0
1.03082894399269 1.2284370225454 0
1.08319563558295 1.1430115893454 0


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