Keyphrases
Augmented Dataset
50%
Binary Classification
50%
CatBoost Classifier
50%
Class Imbalance
100%
Classification Model
50%
Cluster-based
100%
Clustering Methods
50%
Data Synthetization
50%
Decision Tree
50%
Default Prediction
100%
Distribution Difference
12%
Distribution Losses
37%
Ensemble Learning
50%
Family Loans
50%
Freddie Mac
50%
Hierarchical Clustering
50%
Image Repair
100%
Image Text Recognition
12%
Loan Default
50%
Loan Default Prediction
100%
Loss Factor
100%
Loss Function
12%
Minority Class
50%
Model Bias
50%
Model Performance
50%
Multi-layer Perception
50%
Oversampling Technique
100%
Parameter Acquisition
75%
Parameter Reconstruction
12%
Performance Evaluation Index
50%
Performance Prediction
50%
Preprocessing Techniques
50%
Public Loan
50%
Recognition Accuracy
12%
Reconstruction Loss
37%
Repair Method
100%
Repair Models
12%
Simpson's Paradox
50%
Single-family
50%
State-of-the-art Techniques
50%
Step Distribution
12%
Storage Media
100%
Synthetic Approaches
100%
Synthetic Minority Oversampling Technique (SMOTE)
100%
Testing Sample
50%
Text-to-image
100%
Total Loss
12%
Training Samples
50%
XGBoost Classifier
50%
Computer Science
Binary Classification
50%
Class Imbalance
100%
Classification Models
50%
Classification Problem
50%
Decision Trees
50%
Ensemble Learning
50%
Evaluation Metric
50%
Experimental Result
50%
Extreme Gradient Boosting
50%
Generalizability
50%
Hierarchical Clustering
50%
Minority Class
50%
Performance Evaluation
50%
Performance Model
50%
Predictive Performance
50%
Preprocessing Technique
50%
