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Introduction
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Classroom Contents
Building a Naive Bayes Text Classifier with scikit-learn
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- 1 Introduction
- 2 Naive Bayes: A Little History
- 3 Naive Bayes: Advantages and Disavantages
- 4 About the dataset: YouTube Spam Collection
- 5 Pre-requisites
- 6 Naive Bayes: An example
- 7 Naive Bayes: The Equation
- 8 Loading the Dataset
- 9 Train/test split
- 10 Feature extraction: Bag of words approach
- 11 Bag of words approach-Training
- 12 Bag of Words approach-Testing and Evaluation
- 13 Feature Extraction: TF-IDF Approach
- 14 TF-IDF Approach: Training
- 15 TF-IDF Approach: Testing and Evaluation
- 16 Tuning parameters: Laplace smoothing
