Understanding the naïve Bayes algorithm with its implementation in C# - Part 1
Beginning today, we embark on a series of articles focusing on the construction of a naive Bayes classifier in C# with an application to language detection. Our objective is to seamlessly integrate theoretical concepts with practical implementation, providing illustrations of the challenges involved.
What are we aiming to achieve ?
In machine learning, classification is a supervised learning task where the model is trained to categorize input data into predefined classes or labels. Given labeled training data (input-output pairs), a classification algorithm learns to predict the label (class) for new, unseen data.
Classification can be achieved using various algorithms, such as logistic regression, neural networks, and others. In this article, we will focus specifically on the Naïve Bayes technique, a widely used and highly effective method for text classification.
What is language detection ?
Language detection refers to the process of automatically identifying and determining the language in which a given text or document is written. This task is crucial in various applications, such as natural language processing, machine translation, and content filtering.
Common approaches to language detection include statistical methods, machine learning algorithms, and rule-based systems. Statistical methods often involve analyzing character or word frequencies, while machine learning approaches, learn from labeled training data to make predictions about the language of a given text.
In this article, we will therefore utilize the naive Bayes algorithm to perform this task. In reality, the primary objective is not just to create a language classifier but, more importantly, to unveil the intricacies of the Bayes algorithm. We will discover that, despite its seemingly simplistic designation as "naive", it proves to be an exceptionally powerful algorithm.
- Understanding the naïve Bayes algorithm with its implementation in C# - Part 2
- Understanding the naïve Bayes algorithm with its implementation in C# - Part 3
- Understanding the naïve Bayes algorithm with its implementation in C# - Part 4
We referred to the following book to elucidate certain concepts.
Machine Learning: A Probabilistic Perspective (Murphy)
Without further ado and as usual, let's begin with a few prerequisites to correctly understand the underlying concepts. Continue here.