ArticleFrontiers in psychology2024
Predicting implementation of response to intervention in math using elastic net logistic regression.
Article in Frontiers in psychology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Construction of a prediction model for depression risk in elderly patients with chronic obstructive pulmonary disease based on machine learning algorithms and analysis of influencing factors.Scientific reports · 2026Article
- A novel context-discrepancy assessment of impression management facets in autistic and non-autistic adults: an initial validation.Molecular autism · 2026Article
- Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.Alzheimer's research & therapy · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
Introduction: The primary objective of this study was to identify variables that significantly influence the implementation of math Response to Intervention (RTI) at the school level, utilizing the ECLS-K: 2011 dataset. Methods: Due to missing values in the original dataset, a Random Forest algorithm was employed for data imputation, generating a total of 10 imputed datasets. Elastic net logistic regression, combined with nested cross-validation, was applied to each imputed dataset, potentially resulting in 10 models with different variables. Variables for the models derived from the imputed datasets were selected using four methods, leading to four candidate models for final selection. These models were assessed based on their performance of prediction accuracy, culminating in the selection of the final model that outperformed the others. Results and discussion: Method
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