Logistic regression for text in NLP - Model Pipeline Trace

Model Pipeline - Logistic regression for text

This pipeline shows how logistic regression can classify text messages as positive or negative by turning words into numbers and learning from examples.

Data Flow - 5 Stages

1Raw text input

1000 rows x 1 column→Each row is a text message (sentence)→1000 rows x 1 column

"I love this product!"

↓

2Text preprocessing

1000 rows x 1 column→Lowercase, remove punctuation, split into words→1000 rows x 1 column

"i love this product"

↓

3Feature extraction (Bag of Words)

1000 rows x 1 column→Convert words into counts of each word in a fixed vocabulary→1000 rows x 5000 columns

Row vector with counts: [0,1,0,3,...]

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4Train/test split

1000 rows x 5000 columns→Split data into 800 training and 200 testing rows→Training: 800 rows x 5000 columns; Testing: 200 rows x 5000 columns

Training example vector: [0,1,0,3,...]

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5Model training (Logistic Regression)

800 rows x 5000 columns→Learn weights to predict positive or negative label→Model with 5000 weights + bias

Weights vector: [0.01, -0.05, 0.03, ...]

Training Trace - Epoch by Epoch

Loss
0.7 |****
0.6 |*** 
0.5 |**  
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss high, accuracy low
2	0.50	0.75	Loss decreases, accuracy improves
3	0.40	0.82	Model learns important word patterns
4	0.35	0.85	Loss continues to drop, accuracy rises
5	0.32	0.87	Training converges with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input text

Layer 2: Feature extraction

Layer 3: Weighted sum

Layer 4: Sigmoid activation

Layer 5: Prediction

Model Quiz - 3 Questions

What does the feature extraction step do in this pipeline?

A Calculates the loss during training

B Splits data into training and testing sets

C Turns words into numbers representing counts

D Converts probabilities to class labels

Key Insight

Logistic regression can classify text by turning words into numbers and learning weights. The model improves as loss decreases and accuracy increases during training, showing it learns to recognize patterns in word counts.