Model,Best Parameters,CV Best Accuracy,CV Best Precision,CV Best Recall,CV Best F1,CV Best F2,CV Best ROC AUC,CV Best Balanced Accuracy,Test Accuracy,Test Precision,Test Recall,Test F1,Test F2,Test ROC AUC,Test Balanced Accuracy,Tuning Time (seconds),Tuning Log Path
Random Forest,"{""max_depth"": null, ""min_samples_leaf"": 1, ""n_estimators"": 300}",0.690785679921659,0.705858526574826,0.6542525573309949,0.679061188681803,0.663951953699797,0.7605727452084164,0.6907859609897719,0.6352700281545943,0.7316964285714286,0.6654486398700772,0.6970019136721242,0.67772080714522,0.6700048529876521,0.6246218268602464,212.39,/root/ashiravi/predictive_maintenance_project/model_experiments/tuning_logs/random_forest_cv_results.csv
Bagging,"{""max_features"": 1.0, ""max_samples"": 1.0, ""n_estimators"": 200}",0.6900753265167273,0.7059896921609701,0.6516136846639241,0.6776902849535041,0.6617949038954,0.7596725976737501,0.6900751981688193,0.6291272075761454,0.7259358288770054,0.6613885505481121,0.6921606118546845,0.6733630952380952,0.6637432055361174,0.6177441367699009,334.42,/root/ashiravi/predictive_maintenance_project/model_experiments/tuning_logs/bagging_cv_results.csv
Gradient Boosting,"{""learning_rate"": 0.1, ""max_depth"": 5, ""n_estimators"": 100}",0.6602905472323265,0.6728176010753254,0.6241124193621149,0.6475447472326915,0.6332776257812882,0.7147224508561498,0.6602908587428802,0.6370616841566419,0.7547537786445636,0.6285018270401949,0.6858661940629154,0.6502562379232126,0.6881318584299714,0.6400819384508454,182.19,/root/ashiravi/predictive_maintenance_project/model_experiments/tuning_logs/gradient_boosting_cv_results.csv
XGBoost,"{""learning_rate"": 0.1, ""max_depth"": 5, ""n_estimators"": 200}",0.659935119516132,0.6718471025927967,0.6254312377216286,0.6477680745325339,0.6341696561574311,0.7138217599746443,0.6599354618830534,0.6316867161504991,0.7485436893203884,0.6260657734470159,0.6818483307539244,0.6472464741437206,0.6839839036015579,0.6336700058370813,6.39,/root/ashiravi/predictive_maintenance_project/model_experiments/tuning_logs/xgboost_cv_results.csv
AdaBoost,"{""learning_rate"": 0.5, ""n_estimators"": 200}",0.6516645724398202,0.66686482667222,0.6060486061286956,0.6349735959308328,0.6172901927357889,0.70478383092832,0.6516645902739541,0.6316867161504991,0.7549800796812749,0.6155095412099066,0.6781480653097741,0.6391231028667791,0.6948422525960392,0.6373946598016292,54.4,/root/ashiravi/predictive_maintenance_project/model_experiments/tuning_logs/adaboost_cv_results.csv
Decision Tree,"{""criterion"": ""gini"", ""max_depth"": null, ""min_samples_leaf"": 1}",0.6127468787601374,0.6129061480446684,0.6122384502200297,0.612557156747104,0.6123623278474378,0.6127463476190389,0.6127463476190388,0.573841822370105,0.6808703535811423,0.6098254161591555,0.6433925894195759,0.6228230220600431,0.5611453950601871,0.5611453950601872,6.2,/root/ashiravi/predictive_maintenance_project/model_experiments/tuning_logs/decision_tree_cv_results.csv