Number of primary school classes, 2013−2022. by Zhankun Han (20529082)

“…Rural elementary schools in China are scattered, the distribution rate of aesthetic education resources is low, and it is difficult to popularize high-quality aesthetic education resources. (2) The number of classes in China’s rural elementary schools decreased 30.28% from 1.139 million in 2013 to 788,000 in 2022. …”

Investigating the trend of land use class changes. by Abdolhossein Boali (19996390)

Class imbalance on GMSC dataset. by Seongil Han (20490232)

Subjects:

Class distribution balanced through SMOTE oversampling. by Seongil Han (20490232)

Subjects:

Class distribution balanced through NearMiss undersampling. by Seongil Han (20490232)

Subjects:

Based on our time series data(first from the left), the visualizations of linear residual predictions under different decimation rates (next three) show that as the decimation rate... by Hua Huo (1818133)

Linear discriminant analysis (LDA) for effect size (LEfSe) analysis of endophytic bacterial communities associated with <i>Synsepalum dulcificum</i> among the three phenotypic grou... by Rabiath F. R. Adigoun (14189090)

Hypothesis 3: The mean difference between “Class 1” (a school attack happens within <i>k</i> kms. of a given school) and “Class 0” (no school attack happens within <i>k</i> kms. of... by Lirika Sola (21560710)

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

Subjects:

Performance enhancements of classification models using the undersampling method (NearMiss). by Seongil Han (20490232)

Subjects:

Features on GMSC dataset used in NATE. by Seongil Han (20490232)

Subjects:

Performance comparison of AUC, MCC, and F1 across different IR on GMSC dataset. by Seongil Han (20490232)

Subjects:

Searching space for hyperparameters in Table 7. by Seongil Han (20490232)

Subjects:

GMSC dataset (IR: Imbalance Ratio). by Seongil Han (20490232)

Subjects:

Evaluation of benchmark and optimal model performance with resampling techniques. by Seongil Han (20490232)

Subjects:

Local and global interpretability of NATE. by Seongil Han (20490232)

Subjects:

Under-sampled dataset. by Seongil Han (20490232)

Subjects:

The system architecture of NATE. by Seongil Han (20490232)

Subjects:

Performance comparison of machine learning models across accuracy, AUC, MCC, and F1 score on GMSC dataset. by Seongil Han (20490232)

Subjects:

Performance enhancements of classification models using the oversampling method (SMOTE). by Seongil Han (20490232)

Subjects: