Predictive Analytics with TensorFlow 6.4: TF-IDF Model for Predictive analytics

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Information Technology (IT), Architecture
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does TF-IDF stand for in the context of document analysis?
Term Frequency-Inverse Document Frequency
Text Frequency-Index Document Frequency
Text Factor-Inverse Data Frequency
Term Factor-Index Document Factor
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the term frequency (TF) of a word in a document calculated?
By subtracting the word's frequency from the total number of words
By multiplying the word's frequency by the document length
By dividing the number of documents by the number of words
By counting the number of times the word appears in the document
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of the inverse document frequency (IDF) in TF-IDF?
To decrease the importance of rare words
To increase the frequency of common words
To reflect how common or rare a word is across documents
To calculate the total number of words in a document
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which Python module is mentioned as providing a TF-IDF vectorizer?
Matplotlib
NumPy
Pandas
SK learn
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the role of the NLTK module in the context of TF-IDF?
To perform mathematical operations
To provide pre-trained tokenizer models
To manage file input and output
To visualize data
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it unnecessary to use one-hot encoding with TF-IDF?
Because TF-IDF already provides a sparse feature vector
Because TF-IDF is only used for numerical data
Because one-hot encoding is too complex
Because one-hot encoding is outdated
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What might cause a predictive model to perform worse on a test set compared to a training set?
The test set uses a different language
The test set is not preprocessed
The test set has fewer words than the training set
The test set has more words than the training set
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