Improved Accuracy for Identifying At-Risk Students at Different Percentage of Course Length using Logistic Regression Compared with Random Forest Predictive Model.
A. YaminiK. Sashi RekhaPublished in: IC3I (2022)
Keyphrases
- logistic regression
- improved accuracy
- random forest
- decision trees
- predictive model
- random forests
- fold cross validation
- odds ratio
- naive bayes
- ensemble methods
- support vector
- prediction accuracy
- machine learning algorithms
- loss function
- historical data
- machine learning
- neural network
- learning process
- training data
- classification rules
- base classifiers
- feature set
- information extraction
- data mining