Python In Practice - 15 Projects to Master Python - Finding TF and IDF in Extracted Features from Text Data: Text Analyt

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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of creating a count vectorizer?
To translate text data into different languages
To summarize the text data
To convert text data into numerical format for analysis
To delete irrelevant text data
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does term frequency (TF) help in text analysis?
It identifies the most common words in a text
It measures how often a word appears in a document
It translates text into binary code
It removes stop words from the text
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does inverse document frequency (IDF) indicate?
The length of a document
The rarity of a word across multiple documents
The number of sentences in a document
The frequency of a word in a single document
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to use a vectorized format for TF-IDF transformation?
It enhances the aesthetic of the text data
It reduces the size of the text data
It makes the text data more readable
It ensures efficient processing by the transformer
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of the TF-IDF transformer in text analysis?
To calculate both term frequency and inverse document frequency
To delete irrelevant text data
To translate text data into different languages
To summarize the text data
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the final output of the TF-IDF transformation process?
A binary code of the text data
A summary of the text data
A list of the most common words
A sparse matrix representing the text data
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How can TF-IDF be used in machine learning models?
To classify comments or reviews based on sentiment
To translate text data into different languages
To summarize the text data
To delete irrelevant text data
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