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adults.csv
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 1 column, instead of 15 in line 1.
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adults.csv
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feature_selection;transformer;scaler;classifier;acc;f1;auc;acc-std;f1-std;auc-std
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OneHotEncoder(handle_unknown='ignore')();MaxAbsScaler();RandomForestClassifier(max_depth=10);0.8570991545542445;0.6464182542391919;0.9111721808266673;0.003937923192946336;0.011972062128128921;0.0036128618414352025
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OneHotEncoder(handle_unknown='ignore')();MaxAbsScaler();LogisticRegression(max_iter=500);0.8502196702795505;0.6566537522194996;0.9044975994381442;0.003911970436858976;0.010580966368419459;0.004681299180315723
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OneHotEncoder(handle_unknown='ignore')();MaxAbsScaler();KNeighborsClassifier(n_neighbors=10);0.8300115371223156;0.5988939552467304;0.8663463350664052;0.004178080203871286;0.013029652383158986;0.004835438080201718
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OneHotEncoder(handle_unknown='ignore')();MaxAbsScaler();LGBMClassifier();0.8736833316923137;0.7153545054939493;0.9284015211406601;0.002613044997564635;0.007724520269553137;0.001846671038463464
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)();MaxAbsScaler();RandomForestClassifier(max_depth=10);0.8585733088727101;0.6501710083806145;0.9161572938113011;0.003491176779501903;0.01072791452972182;0.002962509175472344
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)();MaxAbsScaler();LogisticRegression(max_iter=500);0.8229784383227496;0.545480639052054;0.8518276496907561;0.0014253615920769886;0.0034475543791673054;0.007810263696293041
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)();MaxAbsScaler();KNeighborsClassifier(n_neighbors=10);0.8300116455805078;0.5944831616801698;0.8668274057397142;0.005333138364545131;0.01551134755451793;0.003090835745280442
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TransformStrategy_OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)();MaxAbsScaler();LGBMClassifier();0.8731611903767593;0.7141831610778255;0.927786337504225;0.0026680224053465614;0.0065240566079071026;0.002531572409427082
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_SingleStrategy();MaxAbsScaler();RandomForestClassifier(max_depth=10);0.8640092523326057;0.6761666612874202;0.9188296744880233;0.00325347910044853;0.006194462584769242;0.003396549762159492
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_SingleStrategy();MaxAbsScaler();LogisticRegression(max_iter=500);0.8498204262425819;0.6559361430996895;0.9035378441723086;0.002794040478609107;0.008870767713204877;0.004757806981098482
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_SingleStrategy();MaxAbsScaler();KNeighborsClassifier(n_neighbors=10);0.8301957840131493;0.5995972705822771;0.8683573425262818;0.004333604997867662;0.012431665377089365;0.005030985530519495
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_SingleStrategy();MaxAbsScaler();LGBMClassifier();0.8738061770995902;0.716174400397257;0.9283995669000731;0.0017337197855733168;0.00482366047622011;0.0023506639269202513
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_SingleStrategy();MaxAbsScaler();DNN;0.8465957519849736;0.5934193270434648;0.9101525938054221;0.0035145301420066635;0.011968516131827561;0.0036501915231057533
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max2Strategy();MaxAbsScaler();RandomForestClassifier(max_depth=10);0.8653605565282213;0.6839808922644294;0.9176611064729465;0.002941374450674707;0.0105938195921649;0.0036396162686327884
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max2Strategy();MaxAbsScaler();LogisticRegression(max_iter=500);0.8504961254961255;0.6572754965129662;0.9039594581157628;0.004082052699743024;0.011936321028650498;0.004860440929784049
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max2Strategy();MaxAbsScaler();KNeighborsClassifier(n_neighbors=10);0.8285681047657096;0.5971419146902125;0.8647732349282231;0.003651085627153151;0.010669332584576948;0.005239379696981348
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max2Strategy();MaxAbsScaler();LGBMClassifier();0.8733148284794992;0.7150620610000686;0.9282142007983646;0.0025798407704896425;0.008461225406532685;0.002621850071936709
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max2Strategy();MaxAbsScaler();DNN;0.8473942824990729;0.6038807536701405;0.9101457472510676;0.004837847425437726;0.02423589489209732;0.0037869637180862666
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max50Strategy();MaxAbsScaler();RandomForestClassifier(max_depth=10);0.8616137503862055;0.6800064112586621;0.9127155820477798;0.0035091789092774237;0.011876220050081585;0.003996586662886315
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max50Strategy();MaxAbsScaler();LogisticRegression(max_iter=500);0.850096824872274;0.6568602799732823;0.9041213341905572;0.004050813865892112;0.010686355698331878;0.0046835780280071035
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max50Strategy();MaxAbsScaler();KNeighborsClassifier(n_neighbors=10);0.8298580121933415;0.5979502592767275;0.863304684038533;0.004204406816146511;0.01438284085762276;0.004560872744009678
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max50Strategy();MaxAbsScaler();LGBMClassifier();0.8741133165534365;0.7174430518889661;0.9286008413585917;0.002699976271417857;0.006624531843810682;0.002333085890911002
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');TFEmbeddingWrapper_Max50Strategy();MaxAbsScaler();DNN;0.8464422789273088;0.5934512888886323;0.9106961476802631;0.006203268483934807;0.02946080889067005;0.0041294765667177465
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');MeanTargetEncoder(alpha=5);MaxAbsScaler();RandomForestClassifier(max_depth=10);0.8648691937614092;0.6777214117618624;0.918333343357235;0.0033462071945243354;0.00885190969583446;0.0033996225672302243
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');MeanTargetEncoder(alpha=5);MaxAbsScaler();LogisticRegression(max_iter=500);0.8454286805334709;0.6399193185359489;0.8997642786537646;0.0022886302906133926;0.008060814383823067;0.004035397347937098
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');MeanTargetEncoder(alpha=5);MaxAbsScaler();KNeighborsClassifier(n_neighbors=10);0.835877437149892;0.6086220405147251;0.8749241328432232;0.003446822419789286;0.009795541349643359;0.0028126730289611606
FeatureSelection(categoricals=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'], numericals=['age', 'fnlwgt', 'education_num', 'capital_gain', 'capital_loss', 'hours-per-week'], target='target');MeanTargetEncoder(alpha=5);MaxAbsScaler();LGBMClassifier();0.8720555628489759;0.7107771270800393;0.9278658188709354;0.0012588367430623067;0.006574138904441795;0.0023448942619947906