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    Home»Machine Learning»🎭 Common vs. Rare: How TF-IDF Finds the Most Important Words | by Ramineni Ravi Teja | Mar, 2025
    Machine Learning

    🎭 Common vs. Rare: How TF-IDF Finds the Most Important Words | by Ramineni Ravi Teja | Mar, 2025

    Team_AIBS NewsBy Team_AIBS NewsMarch 6, 2025No Comments2 Mins Read
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    Think about you stroll into an enormous library with hundreds of thousands of books and ask for “Finest Science Fiction Tales.” The librarian immediately finds essentially the most related books for you.

    Now, think about instructing a pc to do the identical — discovering a very powerful phrases in a sea of textual content. That’s the place TF-IDF is available in!

    Think about you will have a large e-book full of various tales 📖. Every story has plenty of phrases. Now, let’s say you wish to discover out which phrases are a very powerful in every story. That’s the place TF-IDF is available in!

    Consider a narrative about cats 🐱. If the phrase “cat” seems 10 instances, and the overall variety of phrases within the story is 100, we are saying:

    Time period Frequency (TF)

    The extra a phrase seems in a narrative, the upper its TF!

    Now, let’s say we verify 100 completely different tales, and “cat” seems in 90 of them. Meaning “cat” is a standard phrase, so it’s not very particular.

    We calculate IDF like this:

    Inverse Doc Frequency (IDF)

    Since “cat” seems in nearly each story, its IDF is small.
    But when a uncommon phrase, like “unicorn” 🦄, seems in solely 2 tales, its IDF can be excessive!

    Uncommon phrases get the next IDF as a result of they make a narrative distinctive!

    Now, we multiply TF × IDF to search out the significance of every phrase.

    • If a phrase seems rather a lot in a single story however hardly ever in others → Excessive TF-IDF (necessary!)
    • If a phrase seems in nearly each story → Low TF-IDF (not particular).

    For instance:

    • "cat" 🐱 is widespread → Low TF-IDF
    • "microcontroller" 🤖 seems solely in tech tales → Excessive TF-IDF

    Think about we’ve three sentences:

    1️⃣ “I really like pizza and burgers.”
    2️⃣ “Pizza is my favourite meals.”
    3️⃣ “I eat pizza each weekend.”

    • “Pizza” seems in all three sentences → low IDF (widespread phrase).
    • “Burgers” seems solely as soon as → excessive IDF (distinctive phrase).
    • TF-IDF will spotlight “burgers” as an necessary key phrase.

    Think about you’re keen on chocolate bars 🍫.

    • If a retailer has 100 goodies and 10 different candies, goodies are widespread (low IDF).
    • If a retailer has solely 2 goodies, goodies are uncommon and particular (excessive IDF).
    • For those who see a lot of goodies in a single store, however they’re uncommon in different outlets, that store is necessary for chocolate lovers → Excessive TF-IDF! 🎯



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