Awọn ipilẹ Itọsọna
K- Awọn aladugbo to sunmọ
Awọn aladugbo K-Nitosi (KNN) ṣe ipinlẹ aaye data tuntun nipa wiwo awọn apẹẹrẹ K ti o sunmọ julọ ati gbigba ibo to poju.
Gbọ si itọsọna yiiAI-ṣe afihan · ~2 min
Lori iwe yi2 min ka
Akopọ
It matters as one of the simplest, most intuitive algorithms in machine learning, requiring almost no training.
Jin Dive
A KNN classifier stores labeled training examples and, for a query, finds K neighbors under a selected distance metric before combining their labels. A KNN regressor commonly aggregates neighboring numeric targets. It is instance-based rather than a compact parametric model, but fitting may still include feature scaling, index construction, or other preprocessing. Because distance uses every feature, fit scaling on training data so large-range features do not dominate. Brute-force search compares a query with many or all stored examples; KD trees, Ball trees, and approximate-neighbor indexes can accelerate suitable data and metrics. In high dimensions, distance may become less informative and tree indexes may offer little speedup. A small K can be sensitive to noise, while a larger K smooths local decisions. Validate the representation, metric, K, and search method on the intended workload.
Real-World imuse
Awọn ọna ṣiṣe iṣeduro: didaba awọn fiimu tabi awọn ọja ti o jọra si awọn ti olumulo ti nifẹ tẹlẹ.
Idanimọ oni-nọmba ti a fi ọwọ kọ: ṣiṣe iyatọ nọmba kan nipa fiwera si awọn aworan ti o jọra julọ.
Atilẹyin iwadii aisan iṣoogun: asọtẹlẹ ipo ti o da lori awọn alaisan ti o ni awọn abajade idanwo ti o jọra julọ.
Ṣiṣawari atọmọ: gbigba awọn ifibọ ọrọ to sunmọ lati dahun ibeere kan ninu aaye data vector kan.
Ojo iwaju ti K-Nitosi Awọn aladugbo
Ero pataki ti KNN, wa awọn apẹẹrẹ ti o jọra julọ, awọn agbara wiwa fekito ode oni ati iran imupadabọ, nibiti awọn ọna ṣiṣe ti mu awọn ifisinu ifisinu ti o sunmọ julọ si ilẹ awọn awoṣe ede nla. Isunmọ awọn ile ikawe aladuugbo bi FAISS ati HNSW jẹ ki wiwa ibajọra-biliọnu-biliọnu jẹ iwulo. Lakoko ti o ṣọwọn ikasi ikẹhin ni awọn opo gigun ti epo nla, ipilẹ-aladugbo ti o sunmọ jẹ pataki diẹ sii ju igbagbogbo lọ bi ẹhin ti wiwa atunmọ ati iṣeduro.
Fifi o sinu iwa
Kini idi ti o ṣe pataki
- Awọn ipinnu diẹ sii. O ṣe iranlọwọ fun ọ lati ya sọtọ awọn iṣeduro imọ-ẹrọ lati ede tita.
- Iye owo ati isuna. O le beere awọn ibeere imuse to dara julọ ṣaaju lilo owo tabi akoko.
- Ẹgbẹ ati ṣiṣan iṣẹ. Awọn ẹgbẹ pẹlu oye pinpin ṣe ọja to dara julọ, eto imulo, ati awọn ipinnu ikẹkọ.
Ṣọra fun
- Awọn ẹgbẹ oriṣiriṣi le lo ọrọ kanna ni oriṣiriṣi, nitorinaa ṣalaye iwọn ni kutukutu.
- Awọn aṣepari le wo lagbara lakoko ti iṣẹ-aye gidi ko ṣe deede.
- Aibikita didara data ati awọn ero igbelewọn nigbagbogbo ṣẹda awọn abajade ẹlẹgẹ.
Next awọn igbesẹ
- Bẹrẹ pẹlu itumọ-ede itele ti abajade ti o nilo.
- Mu metiriki aṣeyọri kan ati ipo ikuna kan ṣaaju idanwo.
- Ṣiṣe awakọ kekere kan pẹlu data aṣoju, kii ṣe eto demo didan.
- Iwe-ipamọ nibiti awọn aladugbo K-Nitosi ṣe iranlọwọ ati nibiti awọn ọna ti o rọrun dara julọ.
Tesiwaju Ṣiṣawari
Ìwé ìròyìn ọfẹ
Gba ìkànìyàn AI ojoojúmọ́
Ìtàn AI mẹta tó jẹ́rìí ní gbogbo owurọ́ ọjọ́ iṣẹ́, kọ́wé ní Gẹ̀ẹ́sì tó rọrùn. Ọfẹ́ fún gbogbo àkókò, kò sí ìpolówó.
Ìmeèlì kan ní gbogbo ọjọ́ iṣẹ́. Yọkúrò ní ìkànìyàn ní ìkànìyàn kan. A kò ta tàbí pín àdírẹ́sì rẹ.
Ṣe idanwo fun ara rẹ
Take the K-Nearest Neighbors quiz
Ìdáhùn lẹ́sẹ̀kẹsẹ̀ lórí gbogbo ìdáhùn, àti iwe-ẹ̀rí tó lè pin pẹ̀lú ID tó lè jẹ́rìí lẹ́yìn tí o bá kọ́ ẹ̀kọ́.
Ṣe àtìlẹ́yìn ẹ̀kọ́ AI ọfẹ́. AI Understanding jẹ́ àjọ àìní èrè 501(c)(3) — kò sí ìpolówó, kò sí àkúnya owó, ní gbogbo àkókò. Ṣe ẹbun kan
Awọn ibeere ti a beere nigbagbogbo
What is K-Nearest Neighbors?
Awọn aladugbo K-Nitosi (KNN) ṣe ipinlẹ aaye data tuntun nipa wiwo awọn apẹẹrẹ K ti o sunmọ julọ ati gbigba ibo to poju. O ṣe pataki bi ọkan ninu awọn algoridimu ti o rọrun julọ, ogbon inu ni ẹkọ ẹrọ, ko nilo ikẹkọ.
Bawo ni KNN ṣe lẹtọ aaye data tuntun kan?
KNN wa awọn apẹẹrẹ ti o fipamọ K to sunmọ ati fi kilasi ti o wọpọ julọ laarin wọn (fun ipadasẹhin, o ṣe iwọn awọn iye wọn).
Why is KNN often called a “lazy learner”?
KNN is instance-based rather than learning a compact parametric weight set; implementations can still preprocess or build search indexes at fit time.
Kilode ti igbelowọn ẹya ṣe pataki fun KNN?
Nitoripe KNN gbarale ijinna, ẹya ti ko ni iwọn nla le bori awọn miiran, nitorinaa awọn ẹya jẹ deede deede.
Kini yoo ṣẹlẹ ti o ba yan K kekere kan, bii K=1?
K kekere kan jẹ ki aladuugbo kan ti o npariwo tabi ti ko tọ pinnu abajade, ti o yori si jagged, aala ti ko dara.
In a brute-force KNN implementation, why can prediction be slow on a large dataset?
A brute-force scan compares against stored points, while trees or approximate-neighbor indexes can accelerate some workloads depending on data and metric.
Tesiwaju kikọ
Jẹmọ awọn itọsọna
Awọn itọsọna diẹ sii ti a yan fun koko yii