Text feature basics (CountVectorizer, TF-IDF) in ML Python - Model Pipeline Trace

Model Pipeline - Text feature basics (CountVectorizer, TF-IDF)

This pipeline converts text into numbers using CountVectorizer and TF-IDF. Then, it trains a simple model to classify text based on these features.

Data Flow - 5 Stages

1Raw Text Input

6 samples (sentences)→Collect raw text data→6 samples (sentences)

["I love apples", "Apples are tasty", "I hate bananas", "Bananas are yellow", "I love fruit", "Fruit is healthy"]

↓

2CountVectorizer

6 samples (sentences)→Convert text to word count vectors→6 samples x 9 features (unique words)

[[1,1,0,0,0,0,0,0,0], [0,1,1,0,0,0,0,0,0], [1,0,0,1,0,0,0,0,0], [0,0,0,1,1,0,0,0,0], [1,0,0,0,0,1,0,0,0], [0,0,0,0,0,0,1,1,1]]

↓

3TF-IDF Transformer

6 samples x 9 features→Convert counts to TF-IDF scores→6 samples x 9 features (TF-IDF weighted)

[[0.58,0.58,0,0,0,0,0,0,0], [0,0.58,0.81,0,0,0,0,0,0], [0.58,0,0,0.81,0,0,0,0,0], [0,0,0,0.58,0.81,0,0,0,0], [0.58,0,0,0,0,0.81,0,0,0], [0,0,0,0,0,0,0.58,0.58,0.58]]

↓

4Train/Test Split

6 samples x 9 features→Split data into training and testing sets→4 training samples x 9 features, 2 testing samples x 9 features

Train: samples 1,2,3,4; Test: samples 5,6

↓

5Model Training

4 training samples x 9 features→Train a logistic regression classifier→Trained model

Model learns to classify positive vs negative sentiment

Training Trace - Epoch by Epoch

Loss
0.7 | *       
0.6 |  *      
0.5 |   *     
0.4 |    *    
0.3 |     * *  
    +---------
     1 2 3 4  Epoch

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.50	Model starts with random guesses
2	0.48	0.75	Model learns basic word patterns
3	0.35	0.85	Model improves classification accuracy
4	0.30	0.90	Model converges with good accuracy

Prediction Trace - 5 Layers

Layer 1: Input Text

Layer 2: CountVectorizer

Layer 3: TF-IDF Transformer

Layer 4: Model Prediction

Layer 5: Final Decision

Model Quiz - 3 Questions

What does CountVectorizer do to the text data?

A Converts text into TF-IDF scores

B Turns text into counts of each word

C Removes stop words from text

D Splits text into sentences

Key Insight

Converting text into numbers using CountVectorizer and TF-IDF allows machine learning models to understand and classify text data effectively by focusing on important words and their relevance.