Performance metrics for random forest binary classification models. by Alex P. Lind (6945287)

G-clusteron classification of handwritten digits (MNIST) and the resultant distribution of synaptic locations and activations. by Toviah Moldwin (10866708)

Pathways of MOTS-c. by Toheeb Salahudeen (21368040)

Data XGBOOST. by Xin Zhang (35492)

Receiver operating curves for NLP classification. by Charlene Jennifer Ong (8997278)

“…<p>A, stroke presence; B, MCA location; C, acuity. These curves represent different combinations of text featurization (BOW, tf-idf, GloVe) and binary classification algorithms (Logistic Regression, k-NN, CART, OCT, OCT-H, RF, RNN). …”

Candidate predictors by Kexin Qu (10285073)

“…Proportional Odds Logistic Regression (POLR), penalized ordinal regression (RIDGE), classification trees (CART), and random forest (RF) models were built to predict dehydration severity and compared using three ordinal discrimination indices: ordinal c-index (ORC), generalized c-index (GC), and average dichotomous c-index (ADC). …”

Baseline sociodemographic and clinical data by Kexin Qu (10285073)

“…Proportional Odds Logistic Regression (POLR), penalized ordinal regression (RIDGE), classification trees (CART), and random forest (RF) models were built to predict dehydration severity and compared using three ordinal discrimination indices: ordinal c-index (ORC), generalized c-index (GC), and average dichotomous c-index (ADC). …”

Partial dependence plots (A – G) and the resulting clustered feature importance (H) for each feature and trained model. by Daniel Walke (21680915)

“…For each feature, we plotted the average predictions (average ratio of sepsis classification) made by the trained models across different feature values (i.e., grid values). …”

Spontaneous ERG-based random forest model discriminates control and disease cases in rodent models of obesity and type 1 diabetes and predicts disease evolution in the type 1 diabe... by Ramsés Noguez Imm (6658637)

Pan-cancer machine learning predictions of MEKi response. by John P. Lloyd (10196288)

“…<b>Gray boxes</b>: random forest models trained on CCLE-Selumetinib data (<i>f</i>_<i>C2</i>). <b>Regul</b>: regularized regression; <b>RF (reg):</b> regression-based random forest; <b>Logit:</b> logistic regression; <b>RF (bin):</b> classification-based (binary) random forest.…”