Optimal ALARM BN discovered by CausNet. حسب Nand Sharma (21519845)

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Optimal ovarian cancer BNs learned by CausNet and CausNet-partial. حسب Nand Sharma (21519845)

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Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. حسب Tristan Mary-Huard (3864)

Parameter space exploration in comparison to exploitation for the selection of samples during Bayesian Optimization. حسب John E. Fleming (8533956)

Average FDR with and . حسب Nand Sharma (21519845)

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FDR and Runtime for ALARM. حسب Nand Sharma (21519845)

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FDR (). حسب Nand Sharma (21519845)

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FDR (). حسب Nand Sharma (21519845)

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ALARM networks with different partial orderings. حسب Nand Sharma (21519845)

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Average Runtime in seconds. حسب Nand Sharma (21519845)

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Average Hamming Distance with and . حسب Nand Sharma (21519845)

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Average FDR with and . حسب Nand Sharma (21519845)

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Subset lattice for partial orderings with on four nodes . حسب Nand Sharma (21519845)

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Subset lattices with parent set constraints. حسب Nand Sharma (21519845)

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Average Hamming Distance with and . حسب Nand Sharma (21519845)

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Subset lattice for BNs with four nodes . حسب Nand Sharma (21519845)

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Subset lattices with parent set constraints and best sinks identified by CausNet-partial as an example. حسب Nand Sharma (21519845)

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Table_1_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX حسب Maruf A. Tamal (18947776)

"…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …"

Table_2_Unveiling suspicious phishing attacks: enhancing detection with an optimal feature vectorization algorithm and supervised machine learning.DOCX حسب Maruf A. Tamal (18947776)

"…Subsequently, data cleansing, curation, and dimensionality reduction were performed to remove outliers, handle missing values, and exclude less predictive features. To identify the optimal model, the study evaluated and compared 15 SML algorithms arising from different machine learning (ML) families, including Bayesian, nearest-neighbors, decision trees, neural networks, quadratic discriminant analysis, logistic regression, bagging, boosting, random forests, and ensembles. …"

Using BART to Perform Pareto Optimization and Quantify its Uncertainties حسب Akira Horiguchi (11768593)

"…This article proposes Pareto Front (PF) and Pareto Set (PS) estimation methods using Bayesian Additive Regression Trees (BART), which is a nonparametric model whose assumptions are typically less restrictive than popular alternatives, such as Gaussian Processes (GPs). …"