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main_logs.txt
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Spliting data into training set and test set...
Naive imputting missing data...
Preprocessing numerical data...
Preprocessing categorical data...
Smart imputting missing data...
Imputter accuracy score for seen data for education: 0.524558, (base: 0.302478)
Generating data pipeline...
Preprocessing test data and renaming X_train...
Finding best model...
Evaluating XGBClassifier:
- Scores on traing set:
- roc_auc_score - 0.7452
- f1_score - 0.5871
- Scores on test set:
- roc_auc_score - 0.7352
- f1_score - 0.5623
Evaluating MLPClassifier:
- Scores on traing set:
- roc_auc_score - 0.8324
- f1_score - 0.7308
- Scores on test set:
- roc_auc_score - 0.7442
- f1_score - 0.5559
Evaluating SVC:
- Scores on traing set:
- roc_auc_score - 0.6296
- f1_score - 0.3917
- Scores on test set:
- roc_auc_score - 0.6391
- f1_score - 0.4100
Evaluating LogisticRegression:
- Scores on traing set:
- roc_auc_score - 0.6879
- f1_score - 0.4971
- Scores on test set:
- roc_auc_score - 0.7032
- f1_score - 0.5273
Models order (based on roc_auc_score):
- MLPClassifier
- XGBClassifier
- LogisticRegression
- SVC