Questions tagged [k-nearest-neighbors]
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In k-NN, how does the condition $k(N)/N \to 0$ ensure that all the k nearest neighbors are close to a given test point $\mathbf{x}$?
Consider the k-NN algorithm and let $k(N)$ be the choice of k as a function of N (data points).
For $N \to \infty$, if $k(N) \to \infty$ and $k(N)/N \to 0$, then k-NN converges to optimal classifier.
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Meaning of "error on the test point x" in optimal classifier for binary classification
Let f(x) be optimal classifier for binary classification where output is modelled noisy.
What does it mean "f(x) makes a mistake only if there is an error on the test point x"? Basically,
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Is there an ANN vector-search index that supports incremental ingestion and deletion of elements?
I have looked at a few libraries for ANN search of high dimensional vectors. Although impressive, they come with a huge baggage of fine print. Many of them only support ingesting the vectors in one ...
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Nearest neighbour search in high dimension retrieves certain points too often
We represent some catalogue items (documents, music tracks, videos, whatever) as vectors embedded in R^d and use them to retrieve nearest neighbours to users query. The typical scenario is that users ...
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Why does KNN Model return 99% accuracy on dataset with default parameters? [closed]
I am building a model that predicts if a user will like a stock or not based on different features, such as Market Cap, Current Ratio, Sector, Trailing PE, etc. I am going to implement this model in a ...
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Clustering by using Locality sensitive hashing *after* Random projection
It is well known that Random Projection (RP) is tightly linked to Locality Sensitive Hashing (LSH). My goal is to cluster a large number of points lying in a $d$-dimensional Euclidean space, where $d$ ...
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Hierarchical Navigable Small World Graphs : Expected Number of Steps in a Layer
Paper: Efficient and robust approximate nearest
neighbor search using Hierarchical Navigable
Small World graphs
In the Search Complexity section, the author estimates that the expected number of steps ...
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Why the number of training points to densely cover the space grows exponentially with the dimension?
In this lecture (minute 42), the professor says that the number of training examples we need to densely cover the space of training vectors grows exponentially with the dimension of the space. So we ...
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What is the effect of K in K-NN on the VC dimension?
What is the effect of K in K-NN on the VC dimension? When K increases, is the VC dimension decreased or increased, or we can't say anything about this? Is there a reference book that discusses this?
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Is it possible to use k-nearest neighbour for classification with more than two attributes?
If I were to have a dataset of 9 attributes of different types that describe current weather, such as temperature, humidity, etc., and want to classify the current weather by use of a k-NN algorithm, ...
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what is the correct approach for KNN in item based recommendation system?
if I make an application for movies and each user in the system can rate the movies. And I want to make a recommendation system to recommend movies to active user based on his rating for other movies. ...
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How to manually draw a $k$-NN decision boundary with $k=1$ given the dataset and labels?
How to manually draw a $k$-NN decision boundary with $k=1$ knowing the dataset
the labels are
and the euclidean distance between two points is defined as
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Why is KNNBasic better than KNNWithMeans with the default parameters, but KNNWithMeans performs better with folds?
I'm learning a bit about the use of the Surprise library and I have a set of data with users and ratings. I'm training a network with this library, using KNNBasic and KNNWithMeans, this last algorithm ...
