Table 2 This table lists five different machine learning algorithms we evaluated for predicting ovarian cancer from chromosome-scale length variation data using the H2O package in R. The algorithms are ranked by the best AUC it achieved using 5-fold cross validation
From: Genetic risk score for ovarian cancer based on chromosomal-scale length variation
Algorithm | AUC |
|---|---|
Gradient Boosting Machine | 0.88 |
Distributed Random Forest | 0.87 |
Extremely Randomized Trees | 0.86 |
Deep learning | 0.82 |
Generalized Linear Model | 0.68 |