首页|Data from University of Maine at Presque Isle Broaden Understanding of Machine L earning (Exploring Childhood Disabilities in Fragile Families: Machine Learning Insights for Informed Policy Interventions)
Data from University of Maine at Presque Isle Broaden Understanding of Machine L earning (Exploring Childhood Disabilities in Fragile Families: Machine Learning Insights for Informed Policy Interventions)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news reporting from Presque Isle, Maine, by NewsRx journalists, research stated, “This study delves into the multifaceted c hallenges confronting children from vulnerable or fragile families, with a speci fic focus on learning disabilities, resilience (measured by grit), and material hardship-a factor intricately linked with children’s disabilities.” Our news editors obtained a quote from the research from University of Maine at Presque Isle: “Leveraging the predictive capabilities of machine learning (ML), our research aims to discern the determinants of these outcomes, thereby facilit ating evidence-based policy formulation and targeted interventions for at-risk p opulations. The dataset underwent meticulous preprocessing, including the elimin ation of records with extensive missing values, the removal of features with min imal variance, and the imputation of medians for categorical data and means for numerical data. Advanced feature selection techniques, incorporating mutual info rmation, the least absolute shrinkage and selection operator (LASSO), and tree-b ased methods, were employed to refine the dataset and mitigate overfitting. Addi tionally, we addressed the challenge of class imbalance through the implementati on of the Synthetic Minority Over-sampling Technique (SMOTE) to enhance model ge neralization. Various ML models, encompassing Random Forest, Neural Networks [multilayer perceptron (MLP)], Gradient-Boosted Trees (XGBoost ), and a Stacking Ensemble Model, were evaluated on the Future of Families and C hild Wellbeing Study (FFCWS) dataset, with fine-tuning facilitated by Bayesian o ptimization techniques.”
University of Maine at Presque IslePre sque IsleMaineUnited StatesNorth and Central AmericaCyborgsEmerging Te chnologiesMachine Learning
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