Model,Accuracy,AUC,Recall,Prec.,F1,Kappa,MCC,TT (Sec)
Logistic Regression,0.8122,0.86,0.6701,0.8088,0.7308,0.5891,0.5969,0.268
Ridge Classifier,0.8121,0.0,0.6743,0.8075,0.7319,0.5897,0.5977,0.284
Linear Discriminant Analysis,0.8105,0.86,0.687,0.7978,0.7343,0.5888,0.5964,0.254
Extra Trees Classifier,0.8025,0.8548,0.6322,0.8124,0.7035,0.5617,0.5755,0.362
Quadratic Discriminant Analysis,0.732,0.7469,0.4694,0.7386,0.5536,0.3869,0.4118,0.284
Naive Bayes,0.7303,0.8206,0.4272,0.7762,0.5438,0.3769,0.4135,0.25
Random Forest Classifier,0.7238,0.853,0.3645,0.8078,0.4889,0.3449,0.3968,0.305
Light Gradient Boosting Machine,0.7046,0.7528,0.2973,0.8139,0.42,0.2867,0.3499,0.25
Ada Boost Classifier,0.7031,0.8223,0.3226,0.7584,0.4363,0.2908,0.339,0.3
Decision Tree Classifier,0.703,0.6303,0.3183,0.7848,0.4332,0.2894,0.3408,0.27
CatBoost Classifier,0.703,0.8354,0.31,0.7986,0.4337,0.2887,0.3486,0.483
Gradient Boosting Classifier,0.7014,0.7813,0.2931,0.8077,0.4142,0.2793,0.3421,0.302
K Neighbors Classifier,0.6485,0.6318,0.4143,0.5685,0.4721,0.2197,0.2294,0.293
Dummy Classifier,0.6164,0.5,0.0,0.0,0.0,0.0,0.0,0.277
SVM - Linear Kernel,0.5988,0.0,0.3672,0.6084,0.3292,0.1146,0.1667,0.26