| 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 |
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