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This blog walks you through the introduction and implementation of one of the commonly used concept for classification named- “Naive-Bayes Classification”, from Scratch.
INTRODUCTION
In the ever-evolving landscape of artificial intelligence, language models have become increasingly sophisticated, blurring the lines between machine-generated and human-written text. As we navigate through the digital realm, it becomes essential to distinguish between the two for various purposes, from content moderation to maintaining the integrity of information. In this blog post, we’ll explore the application of the Naive Bayes Classifier in discerning text origin, shedding light on the subtle nuances that differentiate language models (LLMs) from human authors.
WHAT IS NAIVE BAYES CLASSIFIER 🤔
The Naive Bayes Classifier is a probabilistic algorithm based on Bayes’ theorem. It assumes independence between features, making it particularly well-suited for text classification tasks. By leveraging statistical patterns and probabilities, this classifier can be trained to recognize distinct traits in the language that set apart LLM-generated content from that crafted by human hands.