model	TrainSet Acc	TestSet Acc	type
ANN	0.9900990099009901	0.9264705882352942	Acc
Linear Regression	0.9356435643564357	0.9411764705882353	Acc
Ridge Regression	0.9356435643564357	0.9411764705882353	Acc
RidgeCV	0.9257425742574258	0.9411764705882353	Acc
Linear Lasso	0.0594059405940594	0.07352941176470588	Acc
Lasso	0.0594059405940594	0.07352941176470588	Acc
ElasticNet	0.0594059405940594	0.07352941176470588	Acc
BayesianRidge	0.9257425742574258	0.9411764705882353	Acc
Logistic Regression	0.9455445544554455	0.9411764705882353	Acc
SGD	0.8564356435643564	0.8529411764705882	Acc
SVM	0.9653465346534653	0.9411764705882353	Acc
KNN	0.9405940594059405	0.9264705882352942	Acc
Naive Bayes	0.9554455445544554	0.9411764705882353	Acc
Decision Tree	1.0	0.9117647058823529	Acc
Bagging	0.9405940594059405	0.9264705882352942	Acc
Random Forest	1.0	0.9411764705882353	Acc
Extra Tree	1.0	0.9558823529411765	Acc
AdaBoost	1.0	0.8823529411764706	Acc
GradientBoosting	1.0	0.9117647058823529	Acc
XGBoost	1.0	0.9558823529411765	Acc
Voting	1.0	0.9411764705882353	Acc
ANN	0.9557017543859649	0.7338709677419355	Precision
Linear Regression	0.7148962148962149	0.7841269841269841	Precision
Ridge Regression	0.7148962148962149	0.7841269841269841	Precision
RidgeCV	0.6737891737891738	0.7841269841269841	Precision
Linear Lasso	0.0297029702970297	0.03676470588235294	Precision
Lasso	0.0297029702970297	0.03676470588235294	Precision
ElasticNet	0.0297029702970297	0.03676470588235294	Precision
BayesianRidge	0.6737891737891738	0.7841269841269841	Precision
Logistic Regression	0.972636815920398	0.9701492537313433	Precision
SGD	0.5982833427655159	0.6188197767145136	Precision
SVM	0.9822335025380711	0.9701492537313433	Precision
KNN	0.47029702970297027	0.4632352941176471	Precision
Naive Bayes	0.8391941391941392	0.7841269841269841	Precision
Decision Tree	1.0	0.6358974358974359	Precision
Bagging	0.47029702970297027	0.4632352941176471	Precision
Random Forest	1.0	0.9701492537313433	Precision
Extra Tree	1.0	0.9772727272727273	Precision
AdaBoost	1.0	0.46153846153846156	Precision
GradientBoosting	1.0	0.6358974358974359	Precision
XGBoost	1.0	0.9772727272727273	Precision
Voting	1.0	0.9701492537313433	Precision
ANN	0.9557017543859649	0.7761904761904761	Recall
Linear Regression	0.7315789473684211	0.7841269841269841	Recall
Ridge Regression	0.7315789473684211	0.7841269841269841	Recall
RidgeCV	0.6872807017543859	0.7841269841269841	Recall
Linear Lasso	0.5	0.5	Recall
Lasso	0.5	0.5	Recall
ElasticNet	0.5	0.5	Recall
BayesianRidge	0.6872807017543859	0.7841269841269841	Recall
Logistic Regression	0.5416666666666666	0.6	Recall
SGD	0.7285087719298247	0.7365079365079366	Recall
SVM	0.7083333333333334	0.6	Recall
KNN	0.5	0.5	Recall
Naive Bayes	0.7030701754385965	0.7841269841269841	Recall
Decision Tree	1.0	0.5841269841269842	Recall
Bagging	0.5	0.5	Recall
Random Forest	1.0	0.6	Recall
Extra Tree	1.0	0.7	Recall
AdaBoost	1.0	0.47619047619047616	Recall
GradientBoosting	1.0	0.5841269841269842	Recall
XGBoost	1.0	0.7	Recall
Voting	1.0	0.6	Recall
ANN	0.9557017543859649	0.7527272727272727	F1
Linear Regression	0.7228496042216359	0.7841269841269841	F1
Ridge Regression	0.7228496042216359	0.7841269841269841	F1
RidgeCV	0.6802110817941952	0.7841269841269841	F1
Linear Lasso	0.05607476635514018	0.06849315068493152	F1
Lasso	0.05607476635514018	0.06849315068493152	F1
ElasticNet	0.05607476635514018	0.06849315068493152	F1
BayesianRidge	0.6802110817941952	0.7841269841269841	F1
Logistic Regression	0.5628565807593942	0.6512820512820513	F1
SGD	0.6226244926882689	0.6458333333333334	F1
SVM	0.7850737194102447	0.6512820512820513	F1
KNN	0.4846938775510204	0.48091603053435117	F1
Naive Bayes	0.7514695830485303	0.7841269841269841	F1
Decision Tree	1.0	0.6015625	F1
Bagging	0.4846938775510204	0.48091603053435117	F1
Random Forest	1.0	0.6512820512820513	F1
Extra Tree	1.0	0.7740863787375416	F1
AdaBoost	1.0	0.46875	F1
GradientBoosting	1.0	0.6015625	F1
XGBoost	1.0	0.7740863787375416	F1
Voting	1.0	0.6512820512820513	F1
