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 ...

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 ...

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 ...

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 ...

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 ...

Multi-Class fNIRS Classification Using an Ensemble of GNN-Based Models Abstract: Functional near-infrared spectroscopy ... whereas the proposed model achieved a classification accuracy of 72.31% for ...

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.

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ...