Features selection using the Boruta algorithm. by Nishat Tasnim Thity (21755858)

Subjects:

Feature selection using Boruta algorithm. by Shayla Naznin (13014015)

“…Feature selection was performed using the Boruta algorithm and model performance was evaluated by comparing accuracy, precision, recall, F1 score, MCC, Cohen’s Kappa and AUROC.…”

Feature selection using the Boruta algorithm. by Guang Tu (22054865)

“…The Boruta algorithm identified 29 significant risk factors, and the top 13 variables were used for developing machine learning models. …”

Comparison of the EODA algorithm with existing algorithms in terms of recall. by Sunil Kumar (102321)

Subjects:

Comparison of the EODA algorithm with existing algorithms in terms of precision. by Sunil Kumar (102321)

Subjects:

Comparison of the EODA algorithm with existing algorithms in terms of F1-Score. by Sunil Kumar (102321)

Subjects:

The proposed algorithm of the outlier detection method. by Sunil Kumar (102321)

Subjects:

Data Sheet 3_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 2_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 4_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.zip by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 6_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.docx by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Data Sheet 1_A prognostic model for highly aggressive prostate cancer using interpretable machine learning techniques.pdf by Cong Peng (160287)

“…Feature selection was performed using the Boruta algorithm, and survival predictions were made using nine machine learning algorithms, including XGBoost, logistic regression (LR), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), decision tree (DT), elastic network (Enet), multilayer perceptron (MLP) and lightGBM. …”

Types of machine learning algorithms. by Md. Merajul Islam (12646837)

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”

ML algorithms in cyber security. by Abdullah Asım Yılmaz (20715311)

Comparison of different optimization algorithms. by Hang Zhao (143592)

Subjects:

Features identified through LASSO analyses and Boruta. by Lang Gao (12963876)

Subjects:

Receiver operator characteristic (ROC) curves of the machine learning algorithms. by Yue Lv (549862)

Data Sheet 1_L-shaped nonlinear relationship between magnesium intake from diet and supplements and the risk of diabetic nephropathy: a cross-sectional study.docx by Jia Du (3363635)

“…A multi-step analytical strategy was adopted: (1) confounders were selected using variance inflation factor and Boruta feature selection algorithm; (2) weighted multivariable logistic regression assessed the association between magnesium intake and DN; (3) restricted cubic splines (RCS), generalized additive models (GAM), and curve fitting were used to evaluate nonlinear dose–response trends; (4) piecewise regression identified potential thresholds; (5) subgroup analyses examined interactions across age, gender, BMI, hypertension, and cardiovascular disease.…”

Data. by Nishat Tasnim Thity (21755858)

Subjects:

The comparison of results from the proposed architecture and state-of-the-art algorithms on the UNSW-B15, NSL-KDD and KDDCUP’99 dataset. by Abdullah Asım Yılmaz (20715311)