Large language models (LLMs), advanced artificial intelligence (AI) models trained to analyze and generate texts in different ...

An innovative system designed to enhance communication for individuals using sign language. Developed in Python, the system leverages a Random Forest classifier to accurately interpret hand movements ...

The hybrid fuzzy–Random Forest model presented here offers a promising approach to capturing that intersection. It is also important to note that the fuzzy inference system was explicitly designed to ...

2.3 Random Forest classifier The second stage involved training a Random Forest classifier using the combined dataset, which included both raw features and the fuzzy-derived Expanded_Score.

The study tested four machine learning algorithms to analyze the data: Gradient tree boosting, random forest, classification and regression trees, or CART, and support vector machine.

The study tested four machine learning algorithms to analyze the data: Gradient tree boosting, random forest, classification and regression trees, or CART, and support vector machine.

This Python project leverages the NBA API and scikit-learn to: Fetch and preprocess NBA player and team statistics for regular seasons. Label seasons with actual MVP winners. Train a Random Forest ...

Designed for high-dimensional datasets, the Boruta algorithm creates shadow features by generating random copies of the original features. It then compares the importance of each real feature to these ...

Class imbalance poses a critical challenge in binary classification problems, particularly when rare but significant events are underrepresented in the training set. While traditional machine learning ...