Train Dreams Cinematographer Adolpho Veloso on Filming Trees Like Actors

Train Dreams is a cinematic exercise in the art of adaptation: Not only did writer-director Clint Bentley adapt Denis Johnson ...
Devdiscourse

Next-gen AI biosensors promise faster, smarter pandemic surveillance

Support vector machines improve classification by mapping inseparable signals into higher-dimensional spaces. Random forest models, through ensemble decision trees, increase robustness against ...
The American Journal of Managed Care

Machine Learning May Enable Earlier Detection of CKD Risk Factors

Both approaches identified hemoglobin as one of the most significant predictors of CKD risk. Additional top-ranked features included blood urea, sodium levels, red blood cell count, potassium, and ...
Visual Studio Magazine

Decision Tree Regression from Scratch Without Pointers or Recursion Using C#

Dr. James McCaffrey presents a complete end-to-end demonstration of decision tree regression from scratch using the C# language. The goal of decision tree regression is to predict a single numeric ...
IEEE

Decision Trees, Random Forests, and Hybrid Ensembles for Ordinal Survey Data Analysis : A Comparative Study on University Responses and Predictive Relationships.

Abstract: This study compares Decision Trees (DT), Random Forests (RF), and hybrid deep learning models on eight years of ordinal university survey data, predicting a key satisfaction question from ...
IEEE

A Comparative Analysis of Random Forest and Decision Tree Classifiers for Predicting Type 2 Diabetes using K-Fold Cross-Validation

Abstract: This study investigates the prediction accuracy of type 2 diabetes using the Pima-Indians-Diabetes dataset, employing a novel classifier that integrates Random Forest and K-fold ...
GitHub

o-ronin-o/decision-trees-random-forest_from_scratch_probabilistic-classifier

Here's the complete project structure: decision-trees-random-forest_from_scratch_probabilistic-classifier/ │ ├── data/ │ ├── raw ...
Scientific Research Publishing

Mohammadagha, M. (2025) Hyperparameter Optimization Strategies for Tree-Based Machine Learning Models Prediction: A Comparative Study of Ada-Boost, Decision Trees, and Random ...

ABSTRACT: This study presents a comprehensive and interpretable machine learning pipeline for predicting treatment resistance in psychiatric disorders using synthetically generated, multimodal data.