Difference between revisions of "K-Nearest Neighbors (KNN)"
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[https://www.google.com/search?q=k-nearest+neighbors+deep+machine+learning+ML ...Google search] | [https://www.google.com/search?q=k-nearest+neighbors+deep+machine+learning+ML ...Google search] | ||
| + | * [[Hierarchical Navigable Small World (HNSW)]] | ||
| + | * [[Inverted File Indexes (IVF)]] | ||
* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]] | ||
** [[...predict categories]] | ** [[...predict categories]] | ||
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* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] | * [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]] | ||
* [https://www.unite.ai/what-is-k-nearest-neighbors/ What Is K-Nearest Neighbors? | Daniel Nelson - Unite.ai] | * [https://www.unite.ai/what-is-k-nearest-neighbors/ What Is K-Nearest Neighbors? | Daniel Nelson - Unite.ai] | ||
Revision as of 10:42, 17 August 2023
YouTube search... ...Google search
- Hierarchical Navigable Small World (HNSW)
- Inverted File Indexes (IVF)
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Optimizer ... Train, Validate, and Test
- Excel ... Documents ... Database; Vector & Relational ... Graph ... LlamaIndex
- What Is K-Nearest Neighbors? | Daniel Nelson - Unite.ai
Can be used for regression or classification by choosing 'K'; where 'K' = # of closest neighboring data points that the model uses in prediction.
This is a simple algorithm which predicts unknown data point with its k nearest neighbors. The value of k is a critical factor here regarding the accuracy of prediction. It determines the nearest by calculating the distance using basic distance functions like Euclidean. However, this algorithm needs high computation power and we need to normalize data initially to bring every data point to same range. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
