Model Pipeline - Bag of Words (CountVectorizer)
This pipeline converts text into numbers using the Bag of Words method. It counts how many times each word appears in the text. Then, a simple model learns to classify the text based on these counts.
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Bag of Words (CountVectorizer) in NLP - Model Pipeline Trace
Data Flow - 5 Stages
1Raw Text Input
5 samples (sentences)→Collect raw sentences as input data→5 samples (sentences)
["I love cats", "Cats are great pets", "Dogs are friendly", "I love dogs", "Pets are family"]
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2Text Preprocessing
5 samples (sentences)→Lowercase and remove punctuation→5 samples (cleaned sentences)
["i love cats", "cats are great pets", "dogs are friendly", "i love dogs", "pets are family"]
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3CountVectorizer (Bag of Words)
5 samples (cleaned sentences)→Convert sentences to word count vectors→5 samples x 8 features (unique words)
[[1,1,0,0,0,0,0,0], [0,1,1,1,1,0,0,0], [0,0,0,1,0,1,1,0], [1,1,0,0,0,0,1,0], [0,0,0,1,1,0,0,1]]
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4Train/Test Split
5 samples x 8 features→Split data into 4 training and 1 test samples→Training: 4 samples x 8 features, Test: 1 sample x 8 features
Training samples: 4 x 8, Test sample: 1 x 8
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5Model Training (Logistic Regression)
4 samples x 8 features→Train model to classify text based on word counts→Trained model
Model learns weights for each word feature
Training Trace - Epoch by Epoch
Loss
0.7 |****
0.6 |***
0.5 |**
0.4 |**
0.3 |*
0.2 |*
0.1 |
+------------
1 2 3 4 5 Epochs| Epoch | Loss ↓ | Accuracy ↑ | Observation |
|---|---|---|---|
| 1 | 0.65 | 0.50 | Model starts with random guesses, accuracy is low |
| 2 | 0.45 | 0.75 | Model learns word importance, accuracy improves |
| 3 | 0.30 | 0.85 | Loss decreases steadily, model fits training data better |
| 4 | 0.20 | 0.90 | Model converges with high accuracy |
| 5 | 0.15 | 0.95 | Final epoch shows best performance |
Prediction Trace - 4 Layers
Layer 1: Input Text
Layer 2: CountVectorizer
Layer 3: Model Prediction (Logistic Regression)
Layer 4: Final Decision
Model Quiz - 3 Questions
Test your understanding
What does the CountVectorizer do to the input text?
Key Insight
The Bag of Words method turns text into simple counts of words. This lets models learn patterns based on word frequency. As training progresses, the model improves by adjusting how much each word influences the prediction.