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 ...

In this paper, we developed and evaluated several models for carrying out multi-label and multi-class text classification. Our approach revolves around the pre-trained BERT models. We endeavour to ...

This project leverages the BERT (Bidirectional Encoder Representations from Transformers) model, a state-of-the-art pre-trained Natural Language Processing (NLP) model developed by Google, to perform ...

The Data Science Lab. Multi-Class Classification Using a scikit Decision Tree. Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the ...

Previous studies on the automatic classification of voice disorders have mostly investigated the binary classification task, which aims to distinguish pathological voice from healthy voice. Using ...

For example, if most of the data items are class moderate (say, 900 out of 1,000) and only a few are class conservative (say, 40 out of 1,000) and class liberal (60 out of 1,000), then a model that ...

AlexNet is one of the popular variants of the convolutional neural network and used as a deep learning framework. In the last article, we implemented the AlexNet model using the Keras library and ...

Multi-class classification: We are categorizing emails into six distinct classes. Multinomial Naïve Bayes supports multi-class classification out of the box, making it a clean fit for this problem.