model	TrainSet Acc	TestSet Acc	type
ANN	0.9936708860759493	0.9811320754716981	Acc
Linear Regression	0.9683544303797469	0.9528301886792453	Acc
Ridge Regression	0.9683544303797469	0.9528301886792453	Acc
RidgeCV	0.9683544303797469	0.9528301886792453	Acc
Linear Lasso	0.04746835443037975	0.03773584905660377	Acc
Lasso	0.04746835443037975	0.03773584905660377	Acc
ElasticNet	0.04746835443037975	0.03773584905660377	Acc
BayesianRidge	0.9683544303797469	0.9528301886792453	Acc
Logistic Regression	0.9936708860759493	0.9811320754716981	Acc
SGD	0.9810126582278481	0.9811320754716981	Acc
SVM	0.9746835443037974	0.9716981132075472	Acc
KNN	0.9525316455696202	0.9716981132075472	Acc
Naive Bayes	0.9683544303797469	0.9716981132075472	Acc
Decision Tree	1.0	0.9433962264150944	Acc
Bagging	0.9525316455696202	0.9811320754716981	Acc
Random Forest	1.0	0.9622641509433962	Acc
Extra Tree	1.0	0.9622641509433962	Acc
AdaBoost	1.0	0.9716981132075472	Acc
GradientBoosting	1.0	0.9433962264150944	Acc
XGBoost	1.0	0.9811320754716981	Acc
Voting	1.0	0.9716981132075472	Acc
ANN	0.9411764705882353	0.8333333333333333	Precision
Linear Regression	0.8250276854928018	0.69009900990099	Precision
Ridge Regression	0.8250276854928018	0.69009900990099	Precision
RidgeCV	0.8250276854928018	0.69009900990099	Precision
Linear Lasso	0.023734177215189875	0.018867924528301886	Precision
Lasso	0.023734177215189875	0.018867924528301886	Precision
ElasticNet	0.023734177215189875	0.018867924528301886	Precision
BayesianRidge	0.8250276854928018	0.69009900990099	Precision
Logistic Regression	0.9966996699669968	0.8700980392156863	Precision
SGD	0.8950166112956811	0.8700980392156863	Precision
SVM	0.9870550161812297	0.9857142857142858	Precision
KNN	0.4762658227848101	0.9857142857142858	Precision
Naive Bayes	0.8107389686337054	0.7950495049504951	Precision
Decision Tree	1.0	0.4807692307692308	Precision
Bagging	0.4762658227848101	0.9903846153846154	Precision
Random Forest	1.0	0.4811320754716981	Precision
Extra Tree	1.0	0.4811320754716981	Precision
AdaBoost	1.0	0.7950495049504951	Precision
GradientBoosting	1.0	0.4807692307692308	Precision
XGBoost	1.0	0.8700980392156863	Precision
Voting	1.0	0.9857142857142858	Precision
ANN	0.9966777408637874	0.9901960784313726	Recall
Linear Regression	0.8250276854928018	0.7352941176470589	Recall
Ridge Regression	0.8250276854928018	0.7352941176470589	Recall
RidgeCV	0.8250276854928018	0.7352941176470589	Recall
Linear Lasso	0.5	0.5	Recall
Lasso	0.5	0.5	Recall
ElasticNet	0.5	0.5	Recall
BayesianRidge	0.8250276854928018	0.7352941176470589	Recall
Logistic Regression	0.9333333333333333	0.8700980392156863	Recall
SGD	0.8950166112956811	0.8700980392156863	Recall
SVM	0.7333333333333334	0.625	Recall
KNN	0.5	0.625	Recall
Naive Bayes	0.8883720930232558	0.8651960784313726	Recall
Decision Tree	1.0	0.49019607843137253	Recall
Bagging	0.5	0.75	Recall
Random Forest	1.0	0.5	Recall
Extra Tree	1.0	0.5	Recall
AdaBoost	1.0	0.8651960784313726	Recall
GradientBoosting	1.0	0.49019607843137253	Recall
XGBoost	1.0	0.8700980392156863	Recall
Voting	1.0	0.625	Recall
ANN	0.9670833333333333	0.895049504950495	F1
Linear Regression	0.8250276854928018	0.7099069512862616	F1
Ridge Regression	0.8250276854928018	0.7099069512862616	F1
RidgeCV	0.8250276854928018	0.7099069512862616	F1
Linear Lasso	0.04531722054380665	0.03636363636363636	F1
Lasso	0.04531722054380665	0.03636363636363636	F1
ElasticNet	0.04531722054380665	0.03636363636363636	F1
BayesianRidge	0.8250276854928018	0.7099069512862616	F1
Logistic Regression	0.9626300851466414	0.8700980392156863	F1
SGD	0.8950166112956812	0.8700980392156863	F1
SVM	0.8116244411326379	0.6927536231884057	F1
KNN	0.4878444084278768	0.6927536231884057	F1
Naive Bayes	0.8445799724572103	0.8259441707717569	F1
Decision Tree	1.0	0.4854368932038835	F1
Bagging	0.4878444084278768	0.8284789644012944	F1
Random Forest	1.0	0.49038461538461536	F1
Extra Tree	1.0	0.49038461538461536	F1
AdaBoost	1.0	0.8259441707717569	F1
GradientBoosting	1.0	0.4854368932038835	F1
XGBoost	1.0	0.8700980392156863	F1
Voting	1.0	0.6927536231884057	F1
