model	TrainSet Acc	TestSet Acc	type
ANN	0.7090909090909091	0.5945945945945946	Acc
Linear Regression	0.6181818181818182	0.6756756756756757	Acc
Ridge Regression	0.6181818181818182	0.6756756756756757	Acc
RidgeCV	0.6181818181818182	0.7837837837837838	Acc
Linear Lasso	0.43636363636363634	0.35135135135135137	Acc
Lasso	0.43636363636363634	0.35135135135135137	Acc
ElasticNet	0.6363636363636364	0.6756756756756757	Acc
BayesianRidge	0.6181818181818182	0.7837837837837838	Acc
Logistic Regression	0.5909090909090909	0.6756756756756757	Acc
SGD	0.5909090909090909	0.5135135135135135	Acc
SVM	0.6909090909090909	0.6756756756756757	Acc
KNN	0.6363636363636364	0.7297297297297297	Acc
Naive Bayes	0.6181818181818182	0.7027027027027027	Acc
Decision Tree	1.0	0.5405405405405406	Acc
Bagging	0.6363636363636364	0.4864864864864865	Acc
Random Forest	1.0	0.4864864864864865	Acc
Extra Tree	1.0	0.5405405405405406	Acc
AdaBoost	1.0	0.5135135135135135	Acc
GradientBoosting	1.0	0.5675675675675675	Acc
XGBoost	1.0	0.5405405405405406	Acc
Voting	0.990909090909091	0.6216216216216216	Acc
ANN	0.8	0.6522435897435898	Precision
Linear Regression	0.6107336956521738	0.6530303030303031	Precision
Ridge Regression	0.6107336956521738	0.6530303030303031	Precision
RidgeCV	0.6107336956521738	0.7651515151515151	Precision
Linear Lasso	0.21818181818181817	0.17567567567567569	Precision
Lasso	0.21818181818181817	0.17567567567567569	Precision
ElasticNet	0.6294157608695652	0.6530303030303031	Precision
BayesianRidge	0.6107336956521738	0.7651515151515151	Precision
Logistic Regression	0.5800000000000001	0.6349206349206349	Precision
SGD	0.5783312047679864	0.41468253968253965	Precision
SVM	0.6918168168168168	0.6530303030303031	Precision
KNN	0.6294157608695652	0.703525641025641	Precision
Naive Bayes	0.6089181286549707	0.6703703703703703	Precision
Decision Tree	1.0	0.5208333333333333	Precision
Bagging	0.6285714285714286	0.48245614035087714	Precision
Random Forest	1.0	0.46577380952380953	Precision
Extra Tree	1.0	0.5208333333333333	Precision
AdaBoost	1.0	0.5175438596491228	Precision
GradientBoosting	1.0	0.5409090909090909	Precision
XGBoost	1.0	0.4866666666666667	Precision
Voting	0.9897959183673469	0.5969696969696969	Precision
ANN	0.7419354838709677	0.6522435897435898	Recall
Linear Regression	0.6095430107526881	0.6618589743589745	Recall
Ridge Regression	0.6095430107526881	0.6618589743589745	Recall
RidgeCV	0.6095430107526881	0.780448717948718	Recall
Linear Lasso	0.5	0.5	Recall
Lasso	0.5	0.5	Recall
ElasticNet	0.6280241935483871	0.6618589743589745	Recall
BayesianRidge	0.6095430107526881	0.780448717948718	Recall
Logistic Regression	0.5571236559139785	0.608974358974359	Recall
SGD	0.5618279569892473	0.4310897435897436	Recall
SVM	0.6717069892473119	0.6618589743589745	Recall
KNN	0.6280241935483871	0.703525641025641	Recall
Naive Bayes	0.6001344086021505	0.6474358974358975	Recall
Decision Tree	1.0	0.5224358974358975	Recall
Bagging	0.6209677419354839	0.4807692307692308	Recall
Random Forest	1.0	0.46314102564102566	Recall
Extra Tree	1.0	0.5224358974358975	Recall
AdaBoost	1.0	0.5192307692307692	Recall
GradientBoosting	1.0	0.5432692307692308	Recall
XGBoost	1.0	0.48717948717948717	Recall
Voting	0.9919354838709677	0.6025641025641025	Recall
ANN	0.701086956521739	0.5945945945945945	F1
Linear Regression	0.6099290780141844	0.6552795031055901	F1
Ridge Regression	0.6099290780141844	0.6552795031055901	F1
RidgeCV	0.6099290780141844	0.7701863354037266	F1
Linear Lasso	0.3037974683544304	0.26	F1
Lasso	0.3037974683544304	0.26	F1
ElasticNet	0.6285038838230328	0.6552795031055901	F1
BayesianRidge	0.6099290780141844	0.7701863354037266	F1
Logistic Regression	0.5387195974280123	0.6118881118881119	F1
SGD	0.5504495504495505	0.41783216783216787	F1
SVM	0.6726190476190477	0.6552795031055901	F1
KNN	0.6285038838230328	0.703525641025641	F1
Naive Bayes	0.5990975355779243	0.6530264279624893	F1
Decision Tree	1.0	0.518007662835249	F1
Bagging	0.6212121212121211	0.47261815453863465	F1
Random Forest	1.0	0.4613026819923372	F1
Extra Tree	1.0	0.518007662835249	F1
AdaBoost	1.0	0.5044642857142858	F1
GradientBoosting	1.0	0.5403726708074534	F1
XGBoost	1.0	0.4865306122448979	F1
Voting	0.9907803201743357	0.5978260869565217	F1
