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Classifying thoracic surgery survival using a quick random forest classifier baseline, deep learning, and SMOTE oversampling to compensate for class imbalance
Data used is from UCI Machine Learning Repository
First decision tree visualization
Model learning curves
Simple dense model
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Using deep learning and random forest classification to predict survival after thoracic surgery