Relevant data underlying the findings described in manuscript. by Lin Qi (128224)

The relevant data file for this article. by Linsha Liu (20378293)

“…Users with higher anxiety and loneliness levels are more likely to use the algorithm matching function of virtual social networking, engage in false self-presentation, and have less trust in the platform. …”

Model-relevant variable data for external patients. by Lei Gao (6566)

Subjects:

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

Subjects:

Relevant computed image data. by Zhaoyu Ku (20660546)

Subjects:

XGBoost odor prediction model: finding the structure-odor relationship of odorant molecules using the extreme gradient boosting algorithm by Pankaj Tyagi (20147683)

“…We first collected the dataset of 1278 odorant molecules with seven basic odor descriptors, and then 1875 physicochemical properties of odorant molecules were calculated. To obtain relevant physicochemical features, a feature reduction algorithm called PCA was also employed. …”

Bagging Model Algorithm Flow. by Wen Yu (384130)

Data Sheet 1_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by Habtamu Setegn Ngusie (12178327)

“…</p>Result<p>The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. …”

Data Sheet 2_Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.pdf by Habtamu Setegn Ngusie (12178327)

“…</p>Result<p>The prevalence of health facility delivery in East Africa was found to be 83.71%. The findings showed that the support vector machine (SVM) algorithm and CatBoost performed best in predicting the place of delivery, in which both of those algorithms scored an accuracy of 95% and an AUC of 0.98 after optimized with Bayesian optimization tuning and insignificant difference between them in all comprehensive analysis of metrics performance. …”

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

Summary of currently used data sets on lie detection. by Dmitri Bershadskyy (14684464)

Subjects:

State-of-the-Art Skin Disease Classification Using Ensemble Learning and Advanced Image Processing by Nirupama (22148684)

“…Then the feature extraction is performed using the Gray Level Co-occurrence Matrix. For classification, the Meta Ensemble-based Random Cat Gradient Boost model is introduced by combining the merits of multiple classifiers to enhance prediction performance. …”

Identification of CRG based on WGCNA algorithm. by Lin Feng (84785)

A directed acyclic graph of Bayesian network obtained through hill-climbing learning algorithm with threshold  by Jaeyeop Choi (12696498)

Table 1_Predicting financial distress in TSX-listed firms using machine learning algorithms.docx by Mark Eshwar Lokanan (20344707)

“…Introduction<p>This study investigates the application of machine learning (ML) algorithms, a subset of artificial intelligence (AI), to predict financial distress in companies. …”

External cohort raw data. by Lei Gao (6566)

Subjects:

Internal cohort raw data. by Lei Gao (6566)

Subjects:

Comparison of the performance of the predictive models using the training dataset. by Fatima Zahra Rhmari Tlemçani (22319928)

Subjects:

Image 9_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.jpeg by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”

Table 1_Using a random forest model to predict volume growth of larch, birch, and their mixed forests in northern China.docx by Junfei Zhang (7547975)

“…<p>Accurately quantifying forest volume and identifying its driving mechanisms are critical for achieving carbon neutrality objectives. Using data from the National Forest Inventory (NFI), plot-level measurements, and environmental variables from pure larch (LP), birch (BP), and mixed larch-birch (LB) forests in the mountainous region of northern Hebei, China, this study employed random forest (RF) algorithms to evaluate the relative importance and partial dependence of biotic and abiotic factors on stand volume growth. …”