Abstract: Random forest is effective and accurate in making predictions for classification and regression problems, which constitute the majority of machine learning applications or systems nowadays.

The Novel Random Forest Classifier outperforms the Stochastic Gradient Descent Classifier, which attained an accuracy of 94.7320 percent. The Independent sample t-test yielded a significant p-value of ...

A collection of Python scripts demonstrating how to run various AI tasks locally using models from the Hugging Face Hub and the transformers library (along with related libraries like datasets, ...