Performance evaluation measure of the machine learning algorithm after data balancing with SMOTE and grid search tuning. by Alemu Birara Zemariam (17540938)

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)

“…</p><p>Results</p><p>Our study included 2,213 patients, of whom 345 (15.6%) experienced in-hospital mortality. The Boruta algorithm identified 29 significant risk factors, and the top 13 variables were used for developing machine learning models. …”

ROC curve analysis of machine learning algorithms with balanced data before tuning (A) and after optimized with hyper parameter tuning (B). by Alemu Birara Zemariam (17540938)

Comparison of imbalanced data handling techniques using accuracy and AUC values. by Alemu Birara Zemariam (17540938)

Prevalence of stunting among adolescent girls in Ethiopia, 2016 with unbalanced data (A) and balanced date using SMOTE (B). by Alemu Birara Zemariam (17540938)

AUW-CE Mining Algorithms & Dataset Hub by Shuaikang Yuan (21091847)

“…<p dir="ltr">High Utility Co-location Pattern Mining (HUCPM), as an important branch of spatial data mining, aims to extract patterns with utility values that meet or exceed a predefined threshold based on user-defined utility criteria (e.g., cost, profit). …”

Sanitized databases using MLHProtector algorithm. by Loan T. T. Nguyen (20660789)

Subjects:

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

Subjects:

Sanitized database obtained from the FMLHProtector algorithm. by Loan T. T. Nguyen (20660789)

Subjects:

The method for determining the chemical constituents of unknown materials in the algorithm. by Dong Hyeok Choi (12562048)

Runtime of the proposed algorithms when varying <i>ξ</i>. by Loan T. T. Nguyen (20660789)

Subjects:

Runtime of the proposed algorithms when varying <i>SP</i>%. by Loan T. T. Nguyen (20660789)

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. …”

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

Subjects: