📅 2025-02-18 — Session: Developed Fast Text Classifier with Scikit-Learn
🕒 21:10–21:55
🏷️ Labels: Text Classification, Scikit-Learn, Logistic Regression, Naïve Bayes, Deep Learning
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The goal of this session was to set up and develop a fast and reliable text classifier using Scikit-Learn, focusing on Logistic Regression and Naïve Bayes models applied to the 20 Newsgroups dataset.
Key Activities
- Fast Text Classification Setup: A step-by-step guide was followed to set up a text classifier using Logistic Regression and Naïve Bayes models. This included installation, data preprocessing, model training, and text classification.
- Systematic Development Approach: A framework was outlined for developing a text classifier, focusing on dataset selection, feature extraction, and model implementation.
- Dataset Exploration: A comprehensive list of datasets suitable for text categorization and general web data classification was reviewed, highlighting their use cases and characteristics.
- Deep Learning Insights: Reflected on the role of perception layers in deep learning models, discussing their applications in feature extraction, classification, and clustering.
Achievements
- Successfully set up the environment for text classification using Scikit-Learn.
- Identified and reviewed suitable datasets for text categorization and web data classification.
- Gained insights into the perception layers of deep learning models for feature extraction.
Pending Tasks
- Implement the classifier on a larger scale and evaluate its performance.
- Explore additional deep learning models for text classification without text generation.