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Grokking-Algorithms book
PostgreSQL
  • Content
  • Introduction to Algorithms
    • Introduction
    • Binary Search
    • Big O notation
    • Recap
  • Selection sort
    • How memory works
    • Arrays and linked lists
    • Selection sort
    • Recap
  • Recursion
    • Recursion
    • Base case and recursive case
    • The stack
    • Recap
  • Quicksort
    • Divide & conquer
    • Quicksort
    • Big O notation revisited
    • Recap
  • Hash tables
    • Hash functions
    • Use cases
    • Collisions
    • Performance
    • Recap
  • Breadth-first search
    • Introduction to graph
    • What is a graph
    • Breadth-first search
    • Implementing the graph
    • Implementing the algorithm
    • Recap
  • Dijkstra's algorithm
    • Working with Dijkstra's algorithm
    • Terminology
    • Trading for a piano
    • Negative-weight edges
    • Implementation
    • Recap
  • Greedy Algorithms
    • The classroom scheduling problem
    • The knapsack problem
    • The set-covering problem
    • NP-complete problems
    • Traveling salesperson, step by step
    • Recap
  • Dynamic programming
    • The knapsack problem
    • Knapsack problem FAQ
    • Longest common substring
    • Recap
  • K-nearest neighbors
    • Classifying oranges vs. grapefruit
    • Building a recommendations system
    • Introduction to machine learning
    • Recap
  • Where to go next
    • Trees
    • Inverted indexes
    • The Fourier transform
    • Parallel algorithms
    • MapReduce
    • Bloom filters and HyperLogLog
    • The SHA algorithms
    • Locality-sensitive hashing
    • Diffie-Hellman key exchange
    • Linear programming
    • Epilogue
  • Answers to exercises
    • Chapter 1
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7
    • Chapter 8
    • Chapter 9
    • Chapter 10
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  1. K-nearest neighbors

Classifying oranges vs. grapefruit

PreviousK-nearest neighborsNextBuilding a recommendations system

Last updated 1 year ago

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Bu mevaga qarang. Bu apelsinmi yoki greyfurtmi? Men greyfurtlarning odatda kattaroq va qizilroq ekanligini bilaman.

Mening fikrlash jarayonim shunday: mening miyamda grafik bor.

Umuman olganda, kattaroq, qizilroq mevalar greyfurtlardir. Bu meva katta va qizil, shuning uchun bu greyfurt bo'lishi mumkin. Ammo bunday mevani olsangiz nima bo'ladi?

Ushbu mevani qanday tasniflagan bo'lardingiz? Buning bir usuli - bu joyning qo'shnilariga qarash. Bu joyning uchta eng yaqin qo'shnisiga qarang.

Greypfrutdan ko'ra ko'proq qo'shnilar apelsindir. Shunday qilib, bu meva, ehtimol, apelsindir. Tabriklaymiz: Siz hozirgina tasniflash uchun k-yaqin qo'shnilar (KNN) algoritmidan foydalandingiz! Butun algoritm juda oddiy.

KNN algoritmi oddiy, ammo foydali! Agar biror narsani tasniflamoqchi bo'lsangiz, avval KNNni sinab ko'rishingiz mumkin. Keling, haqiqiyroq misolni ko'rib chiqaylik.

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