A framework for improving localisation prediction algorithms. by Sven B. Gould (12237287)

“…One can expect that the combination of multi-dimensional parameters from evolutionary biology, cell biology and molecular biology on evolutionary diverse species will significantly improve the next generation of machine leaning algorithms that serve localisation (and function) predictions.…”

Performance of algorithms outside the training species. by Sven B. Gould (12237287)

“…Each Venn diagram of the top panel shows an overlap between predicted (left circles, colour-coded based on the algorithms used) and experimentally verified organelle proteomes (right circles, grey). …”

Ranking of features for each algorithm. by Pritam Chakraborty (9261302)

“…The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

Parameters of each algorithm. by Nanqi Li (11640083)

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Algorithm accuracy comparison for each feature. by Pritam Chakraborty (9261302)

“…The study investigates the use of the Shapley value in predictive ischemic brain stroke analysis. Initially, preference algorithms identify the most important features in various machine learning models, including logistic regression, K-nearest neighbor, decision tree, support vector machine (linear kernel), support vector machine ( RBF kernel), neural networks, etc. …”

The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…Finally, we use the Laplace approximation to determine a lower bound for the out-of-sample prediction error and derive a scalable expression for the marginal variance of each prediction. …”

Evaluation metrics of the models constructed by each algorithm. by Xiaoyan Yang (259084)

One-step trajectory prediction results for the X-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm. by Lin Li (28817)

“…<p>One-step trajectory prediction results for the X-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm.…”

One-step trajectory prediction results for the Z-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm. by Lin Li (28817)

“…<p>One-step trajectory prediction results for the Z-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm.…”

One-step trajectory prediction results for the Y-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm. by Lin Li (28817)

“…<p>One-step trajectory prediction results for the Y-coordinate: (a) one-step prediction error for the basic single algorithm; (b) one-step prediction error for each single algorithm in the algorithm; (c) one-step prediction error comparison for the ensemble prediction algorithm.…”

Data and code resources. by Sam Hall-McMaster (10343795)

Average computation time for each algorithm. by Jinyu Wang (381467)

Computed mean error rate for each algorithm and dataset. by Yubraj Gupta (7465394)

The forecasted MAPE results for the vegetation restoration rate with each algorithm. by Yuan Han (1267323)

Performance metrics evaluation of each algorithm on four diverse datasets. by Yubraj Gupta (7465394)

Error metrics of each regression algorithm using 2021 as control year. by M. Isabel Ramos (20565201)

Pseudocode for the missForestPredict algorithm. by Elena Albu (15181070)

“…Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. …”

Pairwise comparisons and significance tests between algorithms for each regression metric. by Shikun Chen (14625352)

The AUC of each model was calculated using 113 ML algorithm. by Xuefen Lin (17785367)