Relative variable importance for each species distribution model using an increase in AUC values (ΔAUC). by Wanmo Kang (14271530)

“…<p>Relative variable importance for each species distribution model using an increase in AUC values (ΔAUC).</p>…”

Data Sheet 1_Emotional prompting amplifies disinformation generation in AI large language models.docx by Rasita Vinay (21006911)

“…Introduction<p>The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. …”

ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when . by Jen-Hsiang Chen (22339551)

“…<p>ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when .…”

ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when . by Jen-Hsiang Chen (22339551)

“…<p>ARL values for the AIB-GWMA-ME charts with linearly increasing variance method by taking , , , when .…”

AUC values of models. by Yanli Xia (14992547)

Feasibility of AI-powered assessment scoring: Can large language models replace human raters? by Michael Jaworski III (22156096)

“…<p><b>Objective:</b> To assess the feasibility, accuracy, and reliability of using ChatGPT-4.5 (early-access), a large language model (LLM), for automated scoring of Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) protocols. …”

Feature engineering. by Youli Jiang (15249070)

Subjects:

AUC-ROC values. by Vijaykumar S. Jatti (19251057)

Mean AUC values and standard deviation of single models trained using increasing number of features. by Magdalyn E. Elkin (11113840)

“…<p>Mean AUC values and standard deviation of single models trained using increasing number of features.…”

Mean AUC values and standard deviation of ensemble models trained using increasing number of features. by Magdalyn E. Elkin (11113840)

“…<p>Mean AUC values and standard deviation of ensemble models trained using increasing number of features.…”

GPT-4 results in decreased error rate for all tasks with increasing complexity when compared to GPT-3.5-turbo. by Jacqueline A. Jansen (14894276)

STI predictive models’ AUC values. by Owen Mugurungi (230623)

The ROC curves for the four serum proteins with the highest AUC values. by Matilda Holm (5076659)

sUA AUC, cut-off value, sensitivity, specificity. by Jangwon Choi (16612218)

AUC values for different splits between training and test sample. by Janna Axenbeck (10526073)

“…<p>Line plot that illustrates for each indicator how AUC values of the ‘all’ feature model increase if the train/test split changes from (0.1/0.9) to (0.9/0.1) in steps of 0.01.…”

The AUC values of different sampling methods in the cumulative sample size. by Xi Li (19208)

ROC curves and corresponding AUC values for different models in the training set. by Yanxuan Wu (9623598)

Evaluation of AUC. by Juliza Mohamad Arif (11506635)

Data such as specificity, sensitivity and AUC under different cut-off values. by Wenyan Xiao (2737087)

Scatterplot of the mean cross-validated AUC (AUC-cv) and the maximum dispersal distance for the six amphibian species tested. by Damian O. Ortiz-Rodríguez (17343308)