Often when you start learning about classification problems in Machine Learning, you start with binary classification or where there are only two possible outcomes, such as spam or not spam, fraud or ...

Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label ...

MMAC: a new multi-class, multi-label associative classification approach Abstract: Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is ...

This project implements a multi-task learning model for multi-label text classification using BERT. It leverages techniques like early stopping, learning rate warm-up, and gradient accumulation to ...

Multi-class Classification: If the output label has more than two outcomes, it is known as multi-class Classification. E.g., classifying types of music or types of crops. Traditional machine learning ...