enet_search_cv¶

In [ ]:

enet_search_cv(input_relation: (str, vDataFrame),
               X: list,
               y: str,
               metric: str = "auto",
               cv: int = 3,
               estimator_type: str = "auto",
               cutoff: float = -1,
               cursor=None,
               print_info: bool = True,)

Computes the k-fold grid search using multiple ENet models.

Parameters¶

Name	Type	Optional	Description
input_relation	str / vDataFrame	❌	Input Relation.
X	list	❌	List of the predictor columns.
y	str	❌	Response Column.
auto	str / list	✓	Metric used to do the model evaluation. auto : logloss for classification & rmse for regression. For Classification: accuracy : Accuracy auc : Area Under the Curve (ROC) bm : Informedness = tpr + tnr - 1 csi : Critical Success Index = tp / (tp + fn + fp) f1 : F1 Score logloss : Log Loss mcc : Matthews Correlation Coefficient mk : Markedness = ppv + npv - 1 npv : Negative Predictive Value = tn / (tn + fn) prc_auc : Area Under the Curve (PRC) precision : Precision = tp / (tp + fp) recall : Recall = tp / (tp + fn) specificity : Specificity = tn / (tn + fp) For Regression: max : Max Error mae : Mean Absolute Error median : Median Absolute Error mse : Mean Squared Error msle : Mean Squared Log Error r2 : R-squared coefficient r2a : R2 adjusted rmse : Root Mean Squared Error var : Explained Variance
cv	int	✓	Number of folds.
estimator_type	str	✓	Estimator Type. auto : detects if it is a Logit Model or ENet. enet : ElasticNet. logit : Logistic Regression.
cutoff	float	✓	The model cutoff (classification only).
cursor	DBcursor	✓	Vertica database cursor.
print_info	bool	✓	If set to True, prints the model information at each step.

Returns¶

tablesample : An object containing the result. For more information, see utilities.tablesample.

Example¶

In [63]:

from verticapy.learn.model_selection import enet_search_cv
enet_search_cv(input_relation = "public.titanic", 
               X = ["age", "fare", "pclass",], 
               y = "survived", 
               cv = 3)

Starting Bayesian Search

Step 1 - Computing Random Models using Grid Search

Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.0}; Test_score: 0.262598430013598; Train_score: 0.274133719943495; Time: 0.3262985547383626;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.1}; Test_score: 0.266291093427745; Train_score: 0.27198829802802066; Time: 0.30567367871602374;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.2}; Test_score: 0.283669488575714; Train_score: 0.264631430687299; Time: 0.28858145078023273;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.30000000000000004}; Test_score: 0.27765932436013835; Train_score: 0.26790878269621965; Time: 0.28299593925476074;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.4}; Test_score: 0.26681449042856936; Train_score: 0.27091851916276766; Time: 0.3265560468037923;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.5}; Test_score: 0.27761610729421865; Train_score: 0.267879726385803; Time: 0.2933203379313151;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.6000000000000001}; Test_score: 0.2813431410163907; Train_score: 0.26608977303223935; Time: 0.322865088780721;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.7000000000000001}; Test_score: 0.27371067192792264; Train_score: 0.2700078071223963; Time: 0.30130958557128906;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.8}; Test_score: 0.271327358139843; Train_score: 0.269882288039403; Time: 0.2919313907623291;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1e-05, 'l1_ratio': 0.9}; Test_score: 0.26711046643049097; Train_score: 0.272047697007229; Time: 0.2928773562113444;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.0}; Test_score: 0.273844245656181; Train_score: 0.2700430792031203; Time: 0.2918105125427246;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.1}; Test_score: 0.272151260627639; Train_score: 0.2692774444256; Time: 0.38333924611409503;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.2}; Test_score: 0.2744871040194313; Train_score: 0.26851389273173565; Time: 0.3269970417022705;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.30000000000000004}; Test_score: 0.2688611824467447; Train_score: 0.27192487823291334; Time: 0.28541771570841473;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.4}; Test_score: 0.27435674329418536; Train_score: 0.268383530105114; Time: 0.28881271680196124;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.5}; Test_score: 0.265830035357967; Train_score: 0.2735527377207397; Time: 0.2916240692138672;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.6000000000000001}; Test_score: 0.279605985783173; Train_score: 0.268251216607695; Time: 0.30589548746744794;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.7000000000000001}; Test_score: 0.2739471770777597; Train_score: 0.2670199331998773; Time: 0.30471499760945636;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.8}; Test_score: 0.2703962570325967; Train_score: 0.269869664148955; Time: 0.33469128608703613;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.0001, 'l1_ratio': 0.9}; Test_score: 0.2758269760076687; Train_score: 0.2697030352682843; Time: 0.31498289108276367;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.0}; Test_score: 0.27190338015048665; Train_score: 0.269230251184661; Time: 0.3099230130513509;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.1}; Test_score: 0.26978812994657964; Train_score: 0.27011089307188835; Time: 0.3464393615722656;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.2}; Test_score: 0.26971207011780335; Train_score: 0.27168937759203765; Time: 0.33384863535563153;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.30000000000000004}; Test_score: 0.27048662525399503; Train_score: 0.2718552628195923; Time: 0.34309156735738117;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.4}; Test_score: 0.27561221151756465; Train_score: 0.2677155005204437; Time: 0.30875062942504883;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.5}; Test_score: 0.277363557999443; Train_score: 0.2668737724562886; Time: 0.28888877232869464;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.6000000000000001}; Test_score: 0.26937256597372833; Train_score: 0.270576664932445; Time: 0.2807494004567464;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.7000000000000001}; Test_score: 0.27484874663777004; Train_score: 0.26731255041390767; Time: 0.3062567710876465;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.8}; Test_score: 0.2816827539981557; Train_score: 0.2656013436349037; Time: 0.3246612548828125;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.001, 'l1_ratio': 0.9}; Test_score: 0.2734556558445937; Train_score: 0.2699779022837977; Time: 0.3173202673594157;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.0}; Test_score: 0.27666989938940867; Train_score: 0.2674973553989017; Time: 0.32061028480529785;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.1}; Test_score: 0.27155212604688733; Train_score: 0.270093144284725; Time: 0.3126896222432454;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.2}; Test_score: 0.28743409542406867; Train_score: 0.26558637448954536; Time: 0.3156779607137044;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.30000000000000004}; Test_score: 0.2692825221249547; Train_score: 0.27163345183925663; Time: 0.29481307665507;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.4}; Test_score: 0.2650493381412157; Train_score: 0.27301827491543634; Time: 0.300190528233846;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.5}; Test_score: 0.27342520189033465; Train_score: 0.2688040990514273; Time: 0.3456309636433919;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.6000000000000001}; Test_score: 0.2711271874696027; Train_score: 0.270117610779609; Time: 0.3209356466929118;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.7000000000000001}; Test_score: 0.273643629327861; Train_score: 0.268843609497238; Time: 0.31479668617248535;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.8}; Test_score: 0.2721716992041833; Train_score: 0.27088880403254534; Time: 0.3185320695241292;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.01, 'l1_ratio': 0.9}; Test_score: 0.26927092275557035; Train_score: 0.27282144132281666; Time: 0.2949639956156413;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.0}; Test_score: 0.27217083342385534; Train_score: 0.27445455308973; Time: 0.30247847239176434;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.1}; Test_score: 0.27310733478301136; Train_score: 0.2760817661532617; Time: 0.33632763226826984;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.2}; Test_score: 0.278450353908547; Train_score: 0.27430849234964433; Time: 0.33259208997090656;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.30000000000000004}; Test_score: 0.28516833439827; Train_score: 0.2716733424876173; Time: 0.34057267506917316;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.4}; Test_score: 0.282919720495777; Train_score: 0.27618159314262036; Time: 0.3068840503692627;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.5}; Test_score: 0.27864301340771364; Train_score: 0.28125115367144765; Time: 0.32869958877563477;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.6000000000000001}; Test_score: 0.27839218861863735; Train_score: 0.2833007057917167; Time: 0.31580615043640137;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.7000000000000001}; Test_score: 0.284684916058892; Train_score: 0.2786167876189847; Time: 0.31981778144836426;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.8}; Test_score: 0.2793585936308717; Train_score: 0.28332633498493165; Time: 0.3324185212453206;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 0.1, 'l1_ratio': 0.9}; Test_score: 0.286173390763846; Train_score: 0.28017274478347765; Time: 0.30952270825703937;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.0}; Test_score: 0.280064586600843; Train_score: 0.28021343532362897; Time: 0.3500709533691406;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.1}; Test_score: 0.2765281808274337; Train_score: 0.28514168077681135; Time: 0.337546428044637;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.2}; Test_score: 0.286300385490234; Train_score: 0.280090942931885; Time: 0.3949916362762451;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.30000000000000004}; Test_score: 0.28068088366139365; Train_score: 0.28369223510851266; Time: 0.36893900235493976;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.4}; Test_score: 0.28739997988630667; Train_score: 0.28076816781912434; Time: 0.4097263813018799;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.5}; Test_score: 0.2846031716623277; Train_score: 0.28266511174146197; Time: 0.37319596608479816;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.6000000000000001}; Test_score: 0.289253085845893; Train_score: 0.28209080775449435; Time: 0.48996766408284503;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.7000000000000001}; Test_score: 0.2840886885264547; Train_score: 0.2835976878936253; Time: 0.30651140213012695;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.8}; Test_score: 0.28241917685981766; Train_score: 0.2864133195008343; Time: 0.3036954402923584;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 1.0, 'l1_ratio': 0.9}; Test_score: 0.282482662266238; Train_score: 0.28577528837848665; Time: 0.31153329213460285;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.0}; Test_score: 0.282167561374635; Train_score: 0.2814195229554263; Time: 0.2658512592315674;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.1}; Test_score: 0.28684789565701235; Train_score: 0.281715637509305; Time: 0.23003037770589194;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.2}; Test_score: 0.2868454714040623; Train_score: 0.2831129229959413; Time: 0.26296035448710126;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.30000000000000004}; Test_score: 0.28687766808518966; Train_score: 0.28477458801411665; Time: 0.3370985984802246;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.4}; Test_score: 0.28514866627190166; Train_score: 0.28784880620151965; Time: 0.30331945419311523;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.5}; Test_score: 0.28932703887942335; Train_score: 0.28561912196862965; Time: 0.30033214886983234;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.6000000000000001}; Test_score: 0.2891735964934667; Train_score: 0.2892378306821; Time: 0.28902967770894367;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.7000000000000001}; Test_score: 0.29046114055905564; Train_score: 0.29139875314848535; Time: 0.28153061866760254;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.8}; Test_score: 0.291542927628617; Train_score: 0.2904379614371977; Time: 0.30759231249491376;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 5.0, 'l1_ratio': 0.9}; Test_score: 0.293234675793685; Train_score: 0.295116603003443; Time: 0.28280027707417804;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.0}; Test_score: 0.281558807983945; Train_score: 0.285380851526246; Time: 0.20305927594502768;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.1}; Test_score: 0.28565783220454866; Train_score: 0.2882284568581477; Time: 0.20418914159138998;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.2}; Test_score: 0.28727001779710065; Train_score: 0.28908782293762597; Time: 0.2120063304901123;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.30000000000000004}; Test_score: 0.29087542931298566; Train_score: 0.28884189096797236; Time: 0.25333285331726074;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.4}; Test_score: 0.292350971154677; Train_score: 0.29254298986055965; Time: 0.25289201736450195;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.5}; Test_score: 0.2945289262612603; Train_score: 0.29384571343192534; Time: 0.23057778676350912;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.6000000000000001}; Test_score: 0.29914840213658933; Train_score: 0.29946751621913065; Time: 0.22211543718973795;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.7000000000000001}; Test_score: 0.299515315282455; Train_score: 0.2991626296279187; Time: 0.18698430061340332;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.8}; Test_score: 0.301003090760206; Train_score: 0.3009834172613983; Time: 0.2291417916615804;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 10.0, 'l1_ratio': 0.9}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.22884726524353027;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.0}; Test_score: 0.28645930861842234; Train_score: 0.28418485752131134; Time: 0.22722291946411133;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.1}; Test_score: 0.29535330796531867; Train_score: 0.2964046817939983; Time: 0.21848750114440918;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.2}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.19717979431152344;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.30000000000000004}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.22337841987609863;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.4}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.21022280057271323;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.5}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.21184388796488443;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.6000000000000001}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.18712735176086426;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.7000000000000001}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.19541478157043457;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.8}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.19593048095703125;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 50.0, 'l1_ratio': 0.9}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.20261740684509277;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.0}; Test_score: 0.2841366456192043; Train_score: 0.286398474342786; Time: 0.20740699768066406;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.1}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.19916494687398276;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.2}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.21297764778137207;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.30000000000000004}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.2050775686899821;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.4}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.2380808194478353;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.5}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.19531591733296713;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.6000000000000001}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.2661433219909668;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.7000000000000001}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.21566875775655112;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.8}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.21957778930664062;
Model: LogisticRegression; Parameters: {'solver': 'cgd', 'penalty': 'enet', 'C': 100.0, 'l1_ratio': 0.9}; Test_score: 0.301029995663981; Train_score: 0.301029995663981; Time: 0.3620673020680745;

Step 2 - Fitting the RF model with the hyperparameters data

Step 3 - Computing Most Probable Good Models using Grid Search

Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.112}; Test_score: 0.279951046580572; Train_score: 0.2843840354620593; Time: 0.3151123523712158;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.111}; Test_score: 0.28465228542275833; Train_score: 0.28250577726909804; Time: 0.2990090847015381;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.11}; Test_score: 0.28136780941726164; Train_score: 0.282927204694194; Time: 0.3273129463195801;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.109}; Test_score: 0.292524351360777; Train_score: 0.2785090605138967; Time: 0.3239874839782715;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.108}; Test_score: 0.2867461223583447; Train_score: 0.2817534943888157; Time: 0.3574562867482503;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.107}; Test_score: 0.28333598848936997; Train_score: 0.28254275493078834; Time: 0.31406164169311523;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.106}; Test_score: 0.288372257678885; Train_score: 0.279615559473095; Time: 0.30739561716715497;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.105}; Test_score: 0.288898051360634; Train_score: 0.27865162134178534; Time: 0.3076666196187337;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.104}; Test_score: 0.28407667113436; Train_score: 0.281813730131792; Time: 0.32158835728963214;
Model: LogisticRegression; Parameters: {'C': 3.12, 'l1_ratio': 0.103}; Test_score: 0.28317432379688035; Train_score: 0.28219670606502734; Time: 0.3291612466176351;

Bayesian Search Selected Model
Parameters: {'solver': 'cgd', 'penalty': 'enet', 'max_iter': 100, 'l1_ratio': 0.0, 'C': 1e-05, 'tol': 1e-06}; Test_score: 0.262598430013598; Train_score: 0.274133719943495; Time: 0.3262985547383626;

Out[63]:

	avg_score	avg_train_score	avg_time	score_std	score_train_std
1	0.262598430013598	0.274133719943495	0.3262985547383626	0.004349777028265856	0.0009329483973997906
2	0.2650493381412157	0.27301827491543634	0.300190528233846	0.012710799194373928	0.006821677523737474
3	0.265830035357967	0.2735527377207397	0.2916240692138672	0.008771532310074891	0.005104793135187558
4	0.266291093427745	0.27198829802802066	0.30567367871602374	0.0025961266361210866	0.001294377521097551
5	0.26681449042856936	0.27091851916276766	0.3265560468037923	0.002615930172101627	0.001451844666415892
6	0.26711046643049097	0.272047697007229	0.2928773562113444	0.00802428415807891	0.003618651818559264
7	0.2688611824467447	0.27192487823291334	0.28541771570841473	0.003942387463399168	0.0025701159849661117
8	0.26927092275557035	0.27282144132281666	0.2949639956156413	0.004435716915124935	0.0012253735498850634
9	0.2692825221249547	0.27163345183925663	0.29481307665507	0.005964047416055083	0.0029022716058280363
10	0.26937256597372833	0.270576664932445	0.2807494004567464	0.006475790556243237	0.00293052548844658
11	0.26971207011780335	0.27168937759203765	0.33384863535563153	0.012189090637426256	0.005173377903525278
12	0.26978812994657964	0.27011089307188835	0.3464393615722656	0.015045369739235677	0.007804434369256671
13	0.2703962570325967	0.269869664148955	0.33469128608703613	0.002355018366549438	0.002197611487998931
14	0.27048662525399503	0.2718552628195923	0.34309156735738117	0.00973194335302319	0.004411628377393284
15	0.2711271874696027	0.270117610779609	0.3209356466929118	0.003665997576138045	0.0005533505912554856
16	0.271327358139843	0.269882288039403	0.2919313907623291	0.003903673800886551	0.0023082466370493164
17	0.27155212604688733	0.270093144284725	0.3126896222432454	0.005353146450689406	0.0034556369925374288
18	0.27190338015048665	0.269230251184661	0.3099230130513509	0.00392902741592913	0.001785616167235049
19	0.272151260627639	0.2692774444256	0.38333924611409503	0.0011555213634245924	0.0010143975426826545
20	0.27217083342385534	0.27445455308973	0.30247847239176434	0.003661702382906054	0.0014408160506393564
21	0.2721716992041833	0.27088880403254534	0.3185320695241292	0.007877690092349567	0.0029830084197949715
22	0.27310733478301136	0.2760817661532617	0.33632763226826984	0.0022963712633950048	0.0014786427386107486
23	0.27342520189033465	0.2688040990514273	0.3456309636433919	0.0016709719000489226	0.0007785625621636187
24	0.2734556558445937	0.2699779022837977	0.3173202673594157	0.006437652821632398	0.002568392251263787
25	0.273643629327861	0.268843609497238	0.31479668617248535	0.008865246259382125	0.005577331240488202
26	0.27371067192792264	0.2700078071223963	0.30130958557128906	0.004138536830368685	0.0015822078714623896
27	0.273844245656181	0.2700430792031203	0.2918105125427246	0.006618473725790487	0.0031696105030355817
28	0.2739471770777597	0.2670199331998773	0.30471499760945636	0.005107276166812729	0.0037749507526140154
29	0.27435674329418536	0.268383530105114	0.28881271680196124	0.011343327011574651	0.004669419126102212
30	0.2744871040194313	0.26851389273173565	0.3269970417022705	0.010181571433804777	0.005857462307846778
31	0.27484874663777004	0.26731255041390767	0.3062567710876465	0.0017022427716250818	0.0016018596042515292
32	0.27561221151756465	0.2677155005204437	0.30875062942504883	0.005618234994621494	0.0027389549111835436
33	0.2758269760076687	0.2697030352682843	0.31498289108276367	0.003582191282119523	8.342264758661904e-05
34	0.2765281808274337	0.28514168077681135	0.337546428044637	0.004232971663755035	0.0015348959270411154
35	0.27666989938940867	0.2674973553989017	0.32061028480529785	0.009746243613747449	0.005190433759763678
36	0.277363557999443	0.2668737724562886	0.28888877232869464	0.005368379635389694	0.0014484563310489737
37	0.27761610729421865	0.267879726385803	0.2933203379313151	0.003409049284151565	0.002317637743805133
38	0.27765932436013835	0.26790878269621965	0.28299593925476074	0.0007686907995961589	0.0014132043002215468
39	0.27839218861863735	0.2833007057917167	0.31580615043640137	0.0030960193351221806	0.0023865996986673897
40	0.278450353908547	0.27430849234964433	0.33259208997090656	0.006525651984104786	0.001513306090634289
41	0.27864301340771364	0.28125115367144765	0.32869958877563477	0.0028798120348988467	0.0030915363886010456
42	0.2793585936308717	0.28332633498493165	0.3324185212453206	0.00370045032052364	0.0024265343573997055
43	0.279605985783173	0.268251216607695	0.30589548746744794	0.006564749959143225	0.00369740221475518
44	0.279951046580572	0.2843840354620593	0.3151123523712158	0.005068566460196225	0.0034933509278099783
45	0.280064586600843	0.28021343532362897	0.3500709533691406	0.004340040738064074	0.00237488384513383
46	0.28068088366139365	0.28369223510851266	0.36893900235493976	0.00291117562536515	0.0015756135981438732
47	0.2813431410163907	0.26608977303223935	0.322865088780721	0.018739597352263045	0.006106576307069964
48	0.28136780941726164	0.282927204694194	0.3273129463195801	0.004671697121493927	0.0036828703375445513
49	0.281558807983945	0.285380851526246	0.20305927594502768	0.001658728149463313	0.0002480139836948314
50	0.2816827539981557	0.2656013436349037	0.3246612548828125	0.015850818928056454	0.005939926949107826
51	0.282167561374635	0.2814195229554263	0.2658512592315674	0.0025528302874938034	0.001749533778199504
52	0.28241917685981766	0.2864133195008343	0.3036954402923584	0.0031574790431246535	0.0026420732054964775
53	0.282482662266238	0.28577528837848665	0.31153329213460285	0.004054241417202708	0.0031085743857976475
54	0.282919720495777	0.27618159314262036	0.3068840503692627	0.00796337070096159	0.0044530406047341665
55	0.28317432379688035	0.28219670606502734	0.3291612466176351	0.0033915909375232586	0.0031560088713123015
56	0.28333598848936997	0.28254275493078834	0.31406164169311523	0.0019708407485919672	0.0031360954593191532
57	0.283669488575714	0.264631430687299	0.28858145078023273	0.01427116844646146	0.004706238307507307
58	0.28407667113436	0.281813730131792	0.32158835728963214	0.00522888457805044	0.002737562388302744
59	0.2840886885264547	0.2835976878936253	0.30651140213012695	0.0013970631027550343	0.00233961591571665
60	0.2841366456192043	0.286398474342786	0.20740699768066406	0.003128956094215526	0.002421003100361331
61	0.2846031716623277	0.28266511174146197	0.37319596608479816	0.0026228201148882704	0.002467557949697091
62	0.28465228542275833	0.28250577726909804	0.2990090847015381	0.004092751332396212	0.0028206917557600975
63	0.284684916058892	0.2786167876189847	0.31981778144836426	0.008845501137114738	0.0034786934788982706
64	0.28514866627190166	0.28784880620151965	0.30331945419311523	0.0009749247526232906	0.001760905771480469
65	0.28516833439827	0.2716733424876173	0.34057267506917316	0.006852037833531048	0.003736287604119645
66	0.28565783220454866	0.2882284568581477	0.20418914159138998	0.001459062774545813	0.0011943294057966123
67	0.286173390763846	0.28017274478347765	0.30952270825703937	0.006391295580518481	0.0023876100803798196
68	0.286300385490234	0.280090942931885	0.3949916362762451	0.0069940492571017795	0.004503654497867974
69	0.28645930861842234	0.28418485752131134	0.22722291946411133	0.002618742186349219	0.0004182644925671466
70	0.2867461223583447	0.2817534943888157	0.3574562867482503	0.010515452040312366	0.0037754110329093577
71	0.2868454714040623	0.2831129229959413	0.26296035448710126	0.0033353596139516946	0.0039134155315358925
72	0.28684789565701235	0.281715637509305	0.23003037770589194	0.005755294382834107	0.004443260096925231
73	0.28687766808518966	0.28477458801411665	0.3370985984802246	0.0034100265144293277	0.002776003258892111
74	0.28727001779710065	0.28908782293762597	0.2120063304901123	0.0005281687161790027	0.00039881122071910857
75	0.28739997988630667	0.28076816781912434	0.4097263813018799	0.00974407366542605	0.004217400104930424
76	0.28743409542406867	0.26558637448954536	0.3156779607137044	0.01506157259177145	0.004124859339200786
77	0.288372257678885	0.279615559473095	0.30739561716715497	0.002724375889402183	0.0007793662990998128
78	0.288898051360634	0.27865162134178534	0.3076666196187337	0.006566741034096734	0.003945359678055887
79	0.2891735964934667	0.2892378306821	0.28902967770894367	0.0006225921579365956	0.0020841605728100995
80	0.289253085845893	0.28209080775449435	0.48996766408284503	0.00855106008065081	0.004766251865627149
81	0.28932703887942335	0.28561912196862965	0.30033214886983234	0.001207669448783195	0.0013061068060506327
82	0.29046114055905564	0.29139875314848535	0.28153061866760254	0.00024004196694568777	0.0034700340932891306
83	0.29087542931298566	0.28884189096797236	0.25333285331726074	0.0008832790689641943	3.6960071762331854e-05
84	0.291542927628617	0.2904379614371977	0.30759231249491376	0.0006916095352640495	0.0019126863717182426
85	0.292350971154677	0.29254298986055965	0.25289201736450195	0.00016393734812876942	0.0009362926823337595
86	0.292524351360777	0.2785090605138967	0.3239874839782715	0.008690608414202603	0.003601059602967378
87	0.293234675793685	0.295116603003443	0.28280027707417804	0.0006618855445633651	0.0008214793999581614
88	0.2945289262612603	0.29384571343192534	0.23057778676350912	0.00035592270256009175	0.0015725273884638175
89	0.29535330796531867	0.2964046817939983	0.21848750114440918	0.0003337409075589044	0.0005905536118116889
90	0.29914840213658933	0.29946751621913065	0.22211543718973795	0.0009426983305871986	0.0009253941889090591
91	0.299515315282455	0.2991626296279187	0.18698430061340332	0.0003958277275523793	0.0006095270933012482
92	0.301003090760206	0.3009834172613983	0.2291417916615804	4.660066031104051e-05	8.06761598085528e-05
93	0.301029995663981	0.301029995663981	0.22884726524353027	0.0	0.0
94	0.301029995663981	0.301029995663981	0.19717979431152344	0.0	0.0
95	0.301029995663981	0.301029995663981	0.22337841987609863	0.0	0.0
96	0.301029995663981	0.301029995663981	0.21022280057271323	0.0	0.0
97	0.301029995663981	0.301029995663981	0.21184388796488443	0.0	0.0
98	0.301029995663981	0.301029995663981	0.18712735176086426	0.0	0.0
99	0.301029995663981	0.301029995663981	0.19541478157043457	0.0	0.0
100	0.301029995663981	0.301029995663981	0.19593048095703125	0.0	0.0
101	0.301029995663981	0.301029995663981	0.20261740684509277	0.0	0.0
102	0.301029995663981	0.301029995663981	0.19916494687398276	0.0	0.0
103	0.301029995663981	0.301029995663981	0.21297764778137207	0.0	0.0
104	0.301029995663981	0.301029995663981	0.2050775686899821	0.0	0.0
105	0.301029995663981	0.301029995663981	0.2380808194478353	0.0	0.0
106	0.301029995663981	0.301029995663981	0.19531591733296713	0.0	0.0
107	0.301029995663981	0.301029995663981	0.2661433219909668	0.0	0.0
108	0.301029995663981	0.301029995663981	0.21566875775655112	0.0	0.0
109	0.301029995663981	0.301029995663981	0.21957778930664062	0.0	0.0
110	0.301029995663981	0.301029995663981	0.3620673020680745	0.0	0.0

Rows: 1-110 | Columns: 6