model	TrainSet Acc	TestSet Acc	type
ANN	0.8888888888888888	0.75	Acc
Linear Regression	0.8	0.875	Acc
Ridge Regression	0.8	0.875	Acc
RidgeCV	0.8	0.875	Acc
Linear Lasso	0.7555555555555555	0.875	Acc
Lasso	0.7555555555555555	0.875	Acc
ElasticNet	0.8	0.875	Acc
BayesianRidge	0.8	0.875	Acc
Logistic Regression	0.7555555555555555	0.875	Acc
SGD	0.7111111111111111	0.9375	Acc
SVM	0.8222222222222222	0.875	Acc
KNN	0.8	0.9375	Acc
Naive Bayes	0.7777777777777778	0.9375	Acc
Decision Tree	1.0	0.875	Acc
Bagging	0.7333333333333333	0.875	Acc
Random Forest	1.0	0.8125	Acc
Extra Tree	1.0	0.75	Acc
AdaBoost	1.0	0.8125	Acc
GradientBoosting	1.0	0.8125	Acc
XGBoost	1.0	0.8125	Acc
Voting	1.0	0.875	Acc
ANN	0.8649732620320856	0.42857142857142855	Precision
Linear Regression	0.7446524064171123	0.75	Precision
Ridge Regression	0.7446524064171123	0.75	Precision
RidgeCV	0.7446524064171123	0.75	Precision
Linear Lasso	0.6844919786096256	0.75	Precision
Lasso	0.6844919786096256	0.75	Precision
ElasticNet	0.7446524064171123	0.75	Precision
BayesianRidge	0.7446524064171123	0.75	Precision
Logistic Regression	0.6805555555555556	0.4375	Precision
SGD	0.65	0.8333333333333333	Precision
SVM	0.8269230769230769	0.4375	Precision
KNN	0.8	0.9666666666666667	Precision
Naive Bayes	0.7211981566820276	0.9666666666666667	Precision
Decision Tree	1.0	0.7142857142857143	Precision
Bagging	0.6280487804878049	0.4375	Precision
Random Forest	1.0	0.6282051282051282	Precision
Extra Tree	1.0	0.42857142857142855	Precision
AdaBoost	1.0	0.43333333333333335	Precision
GradientBoosting	1.0	0.6282051282051282	Precision
XGBoost	1.0	0.6282051282051282	Precision
Voting	1.0	0.7142857142857143	Precision
ANN	0.8446969696969697	0.42857142857142855	Recall
Linear Regression	0.7310606060606061	0.9285714285714286	Recall
Ridge Regression	0.7310606060606061	0.9285714285714286	Recall
RidgeCV	0.7310606060606061	0.9285714285714286	Recall
Linear Lasso	0.6742424242424243	0.9285714285714286	Recall
Lasso	0.6742424242424243	0.9285714285714286	Recall
ElasticNet	0.7310606060606061	0.9285714285714286	Recall
BayesianRidge	0.7310606060606061	0.9285714285714286	Recall
Logistic Regression	0.6477272727272727	0.5	Recall
SGD	0.6704545454545454	0.9642857142857143	Recall
SVM	0.6931818181818182	0.5	Recall
KNN	0.6515151515151515	0.75	Recall
Naive Bayes	0.7424242424242424	0.75	Recall
Decision Tree	1.0	0.7142857142857143	Recall
Bagging	0.5530303030303031	0.5	Recall
Random Forest	1.0	0.6785714285714286	Recall
Extra Tree	1.0	0.42857142857142855	Recall
AdaBoost	1.0	0.4642857142857143	Recall
GradientBoosting	1.0	0.6785714285714286	Recall
XGBoost	1.0	0.6785714285714286	Recall
Voting	1.0	0.7142857142857143	Recall
ANN	0.853990914990266	0.42857142857142855	F1
Linear Regression	0.7371836469824788	0.7948717948717948	F1
Ridge Regression	0.7371836469824788	0.7948717948717948	F1
RidgeCV	0.7371836469824788	0.7948717948717948	F1
Linear Lasso	0.6787800129785854	0.7948717948717948	F1
Lasso	0.6787800129785854	0.7948717948717948	F1
ElasticNet	0.7371836469824788	0.7948717948717948	F1
BayesianRidge	0.7371836469824788	0.7948717948717948	F1
Logistic Regression	0.6583850931677019	0.4666666666666667	F1
SGD	0.6560846560846563	0.8814814814814815	F1
SVM	0.7222222222222222	0.4666666666666667	F1
KNN	0.6736502820306205	0.8160919540229885	F1
Naive Bayes	0.7295673076923077	0.8160919540229885	F1
Decision Tree	1.0	0.7142857142857143	F1
Bagging	0.5439189189189189	0.4666666666666667	F1
Random Forest	1.0	0.6444444444444445	F1
Extra Tree	1.0	0.42857142857142855	F1
AdaBoost	1.0	0.4482758620689655	F1
GradientBoosting	1.0	0.6444444444444445	F1
XGBoost	1.0	0.6444444444444445	F1
Voting	1.0	0.7142857142857143	F1
