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What is a bag-of-words model and why is it useful for machine learning algorithms?
What is a bag-of-words model and why is it useful for machine learning algorithms?
A bag-of-words model is a representation of text that converts textual data into numerical matrices, making it usable by machine learning algorithms. It is useful because most machine learning algorithms cannot work directly with non-numerical data.
Why does the bag-of-words model not care about the internal ordering of words in a sentence?
Why does the bag-of-words model not care about the internal ordering of words in a sentence?
The bag-of-words model does not care about the internal ordering of words in a sentence because it aims to extract features from the text and does not consider the sequence or order of the words.
What is Feature Extraction and how does it relate to the bag-of-words model?
What is Feature Extraction and how does it relate to the bag-of-words model?
Feature Extraction is the process of converting input data into a vector of numerical feature values. The bag-of-words model is a feature extraction technique for textual data, aiming to extract features from the text which can be further used in modeling.
What encoding methods are commonly used to convert textual data into numerical matrices for machine learning algorithms?
What encoding methods are commonly used to convert textual data into numerical matrices for machine learning algorithms?
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Provide an example to illustrate how the bag-of-words model handles the ordering of words in a sentence.
Provide an example to illustrate how the bag-of-words model handles the ordering of words in a sentence.
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