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

“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”

Residual plots for a) Support Vector Regression, b) Decision Tree Regression, c) Random Forest Regression, d) XGBoost Regression, e) CatBoost Regression, f) AdaBoost Regression, g)... by Akshansh Mishra (21001499)

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ROC plots were obtained for a) Logistic, b) K-Nearest Neighbours, c) Support Vector, d) Stochastic Gradient Descent, e) Decision Tree, f) Random Forest, g) AdaBoost, h) Gradient Bo... by Akshansh Mishra (21001499)

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Confusion matrix plots obtained for a) Logistic, b) K-Nearest Neighbours, c) Support Vector, d) Stochastic Gradient Descent, e) Decision Tree, f) Random Forest, g) AdaBoost, h) Gra... by Akshansh Mishra (21001499)

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Face detection process based on AdaBoost algorithm. by Yingying Mei (13817440)

“…<p>Face detection process based on AdaBoost algorithm.</p>…”

Q-Q plots for a) Support Vector Regression, b) Decision Tree Regression, c) Random Forest Regression, d) XGBoost Regression, e) CatBoost Regression, f) AdaBoost Regression, g) Extr... by Akshansh Mishra (21001499)

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Actual vs Predicted value plots for a) Support Vector Regression, b) Decision Tree Regression, c) Random Forest Regression, d) XGBoost Regression, e) CatBoost Regression, f) AdaBoo... by Akshansh Mishra (21001499)

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Schematic diagram of the SARIMA algorithm flow. by Yongxin Dai (13796385)

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Noise reduction by the sliding average algorithm. by Yongxin Dai (13796385)

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Prediction of pharmaceuticals occurrence based on sales data and Machine learning algorithms. by Júlia Bijos (14489543)

“…</p><p dir="ltr"><b>Antibioticos/Carbamazepina</b>: contains the main codes of the prediction models to classify the occurrence concentrations of some antibiotics and Carbamazepine, by tree boosting algorithms.</p><p dir="ltr"><b><u>Sensitivity Analysis</u></b></p><p dir="ltr"><b>cargas25_50_75_AS:</b> contains the pre processed updated dataset of pharmaceuticals sales data for the 3 scenarium conditions.…”

The data processing and analysis pipeline. by Xin Zhang (35492)

Uniform Manifold Approximation and Projection (UMAP)-based sorting enhances detection of low-firing rate neurons and boosts mutual information. by Daniel Suárez-Barrera (22676654)

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Flow chart of the HINGO-LSSVM-IAdaBoost algorithm. by Yang Guo (399435)

Flowchart diagram illustrating the data analysis process using machine learning models. by Aline Xavier Ferraz (19544919)

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The weight optimization process. by Guomei Cui (20721578)

“…Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. …”

Gradient boosting machine algorithm main parameters and results. by Wen Zhu (149702)

Algorithm process. by Wei Cui (92129)

Results of gradient boosting classifier. by Balraj Preet Kaur (20370832)

Gradient boosting classifier results. by Balraj Preet Kaur (20370832)

Boost converter topology. by Itzel Contreras Carmona (20427867)