TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used to reflect how important a word is to a document in a collection or corpus.

Here's a step-by-step explanation:

1. Term Frequency (TF): This measures the frequency of a word in a document. If a word appears more times in a document, its significance is higher. It is calculated as:

TF(word) = (Number of times the word appears in a document) / (Total number of words in the document)

2. Inverse Document Frequency (IDF): This measures the importance of a word in the entire corpus. If a word is common or rare across all documents, it is less important. It is calculated as:

IDF(word) = log_e(Total number of documents / Number of documents with the word in it)

3. TF-IDF is then calculated as: TF(word) * IDF(word)

The higher the TF*IDF score (weight), the rarer the term and vice versa.

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TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used to reflect how important a word is to a document in a collection or corpus.

Here's a step-by-step explanation:

1. Term Frequency (TF): This measures the frequency of a word in a document. If a word appears more times in a document, its significance is higher. It is calculated as:

TF(word) = (Number of times the word appears in a document) / (Total number of words in the document)

2. Inverse Document Frequency (IDF): This measures the importance of a word in the entire corpus. If a word is common or rare across all documents, it is less important. It is calculated as:

IDF(word) = log_e(Total number of documents / Number of documents with the word in it)

3. TF-IDF is then calculated as: TF(word) * IDF(word)

The higher the TF*IDF score (weight), the rarer the term and vice versa.

Knowee AI · Accepted Answer

TF-IDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used to reflect how important a word is to a document in a collection or corpus.

Here's a step-by-step explanation:

1. Term Frequency (TF): This measures the frequency of a word in a document. If a word appears more times in a document, its significance is higher. It is calculated as:

TF(word) = (Number of times the word appears in a document) / (Total number of words in the document)

2. Inverse Document Frequency (IDF): This measures the importance of a word in the entire corpus. If a word is common or rare across all documents, it is less important. It is calculated as:

IDF(word) = log_e(Total number of documents / Number of documents with the word in it)

3. TF-IDF is then calculated as: TF(word) * IDF(word)

The higher the TF*IDF score (weight), the rarer the term and vice versa.

TF - IDF represents

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