Close Menu
    Trending
    • How generative AI could help make construction sites safer
    • PCA and SVD: The Dynamic Duo of Dimensionality Reduction | by Arushi Gupta | Jul, 2025
    • 5 Ways Artificial Intelligence Can Support SMB Growth at a Time of Economic Uncertainty in Industries
    • Microsoft Says Its AI Diagnoses Patients Better Than Doctors
    • From Reporting to Reasoning: How AI Is Rewriting the Rules of Data App Development
    • Can AI Replace Doctors? How Technology Is Shaping Healthcare – Healthcare Info
    • Singapore police can now seize bank accounts to stop scams
    • How One Founder Is Rethinking Supplements With David Beckham
    AIBS News
    • Home
    • Artificial Intelligence
    • Machine Learning
    • AI Technology
    • Data Science
    • More
      • Technology
      • Business
    AIBS News
    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
    Share Facebook Twitter Pinterest LinkedIn Tumblr Reddit Telegram Email
    Share
    Facebook Twitter LinkedIn Pinterest Email


    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! 🎯



    Source link

    Share. Facebook Twitter Pinterest LinkedIn Tumblr Email
    Previous ArticleCeramic.ai Emerges from Stealth, Reports 2.5x Faster Model Training
    Next Article Kubernetes — Understanding and Utilizing Probes Effectively
    Team_AIBS News
    • Website

    Related Posts

    Machine Learning

    PCA and SVD: The Dynamic Duo of Dimensionality Reduction | by Arushi Gupta | Jul, 2025

    July 2, 2025
    Machine Learning

    Can AI Replace Doctors? How Technology Is Shaping Healthcare – Healthcare Info

    July 2, 2025
    Machine Learning

    Is Your AI Whispering Secrets? How Scientists Are Teaching Chatbots to Forget Dangerous Tricks | by Andreas Maier | Jul, 2025

    July 2, 2025
    Add A Comment
    Leave A Reply Cancel Reply

    Top Posts

    How generative AI could help make construction sites safer

    July 2, 2025

    I Tried Buying a Car Through Amazon: Here Are the Pros, Cons

    December 10, 2024

    Amazon and eBay to pay ‘fair share’ for e-waste recycling

    December 10, 2024

    Artificial Intelligence Concerns & Predictions For 2025

    December 10, 2024

    Barbara Corcoran: Entrepreneurs Must ‘Embrace Change’

    December 10, 2024
    Categories
    • AI Technology
    • Artificial Intelligence
    • Business
    • Data Science
    • Machine Learning
    • Technology
    Most Popular

    Thousands report Spotify problems around the world

    April 16, 2025

    Artificial Intelligence Concerns & Predictions For 2025

    December 10, 2024

    Silicon Data Launches First GPU Rental Price Index

    May 28, 2025
    Our Picks

    How generative AI could help make construction sites safer

    July 2, 2025

    PCA and SVD: The Dynamic Duo of Dimensionality Reduction | by Arushi Gupta | Jul, 2025

    July 2, 2025

    5 Ways Artificial Intelligence Can Support SMB Growth at a Time of Economic Uncertainty in Industries

    July 2, 2025
    Categories
    • AI Technology
    • Artificial Intelligence
    • Business
    • Data Science
    • Machine Learning
    • Technology
    • Privacy Policy
    • Disclaimer
    • Terms and Conditions
    • About us
    • Contact us
    Copyright © 2024 Aibsnews.comAll Rights Reserved.

    Type above and press Enter to search. Press Esc to cancel.