Machine Learning techniques for Text Classification

This repository will contain common ML techniques for text classification.

In text classification, we are given a document and a fixed set of classes. Classes are also called categories or labels. Our goal is to design algorithms or build models to map documents to classes.

The dataset is part of "The 20 newsgroups text dataset", the words are splited and counted for easy experiments in text applications of machine learning techniques.

Methods of text classification

• Hand-coded rule-based classifiers
• Supervised learning
  • Naive Bayes (simple, common)
  • k-Nearest Neighbors (simple, powerful)
  • Support-vector machines (newer, generally more powerful)
  • Decision trees -> random forests -> gradient-boosted decision trees (e.g., xgboost)
  • Deep Learning
    • Multi Layer Perceptron (MLP)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)