Recall & Review
beginner
What is the main goal of a text classification pipeline?
To automatically assign categories or labels to text data based on its content.
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beginner
Name the typical steps in a text classification pipeline.
1. Text preprocessing (cleaning, tokenization)<br>2. Feature extraction (e.g., converting text to numbers)<br>3. Model training<br>4. Model evaluation<br>5. Prediction on new text
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beginner
Why do we convert text into numbers in a text classification pipeline?
Because machine learning models understand numbers, not raw text. Converting text into numbers allows models to learn patterns.
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beginner
What is tokenization in text preprocessing?
Tokenization is splitting text into smaller pieces called tokens, usually words or phrases, to analyze the text better.
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beginner
How do we evaluate the performance of a text classification model?
By using metrics like accuracy, precision, recall, and F1-score to see how well the model predicts correct labels.
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What is the first step in a text classification pipeline?
✗ Incorrect
Text preprocessing cleans and prepares the text before any modeling.
Which method converts text into numbers for machine learning?
✗ Incorrect
Feature extraction transforms text into numerical features usable by models.
Which metric measures the overall correctness of a text classification model?
✗ Incorrect
Accuracy shows the percentage of correct predictions out of all predictions.
What does tokenization do?
✗ Incorrect
Tokenization breaks text into tokens like words for easier analysis.
Which step comes after feature extraction in a text classification pipeline?
✗ Incorrect
After features are ready, the model is trained on them.
Describe the main steps involved in a text classification pipeline and why each step is important.
Think about how raw text becomes a label prediction.
You got /6 concepts.
Explain why converting text into numerical features is necessary for machine learning models.
Consider how computers process information.
You got /3 concepts.