Questions tagged [k-nearest-neighbour]

k-Nearest-Neighbor Classifiers These classifiers are memory-based, and require no model to be fit. Given a query point x0, we find the k training points x(r),r = 1,...,k closest in distance to x0, and then classify using majority vote among the k neighbors.

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How to determine the discount percentage of a product for a given product category and brand?

We are performing the analysis of data of an online shopping site. Please refer to the dataset mentioned in this link The fields of the dataset are: We have been asked to do the following: Perform ...
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Can I use KNN imputation when data are not MAR (Missing at Random)

I have a dataset with 891 observations and 12 variables. From them, 2 have NA values (V1 has 20% NA and V2 has 77% NA). First I examined if the data are MAR. So, I have created 2 new variables named ...
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Learning distance metric from output of knn

Consider a set $\mathcal X$ of points $\{x_1,\dots,x_n,x_{n+1},\dots,x_{n+m} \}\subset \mathbb R^p$. Let $A$ be some $p\times p$ matrix, unknown to you. Consider the set $$\mathcal X_A:=\{y_1,\dots,y_{...
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Is Hierarchical clustering a special case of knn(specific n=1)?

I'am working on time series in the scope of similarity detection at the moment. What seems to be a well researched approach is dynamic time warping in combination with k-1NN as classification ...
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Is it possible to perform kNN imputation for missing values in time-series data?

I have energy consumption data containing the time values and and corresponding consumption values in kWh. I want to perform imputation in R.
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Reduce scoring time for test sample of K-Nearest Neighbors regression for a time series dependent variables

My dependent variable is values of $ balance amount for an entity over next 24 months after the entity is subjected to a specific treatment. I am trying to predict these 24 values (bal1-bal24) for ...
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What is the VC dimension of k-nearest-neighbours with k=1?

I would answer that it is $\infty$, but I have a gut feeling this may not be the correct answer... May I present my proof attempt that it is indeed $\infty$, so that you can clear any misconceptions ...
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Time complexity for locality sensitive hashing similar image search

I am trying to find most visually similar images for large image dataset. (N=1 million), using LSH (Locality Sensitive Hashing). Image feature vectors are 4096 dimensional VGG-16 features. Now, my ...
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49 views

R prcomp and KNN different correct classification rate

I'm performing a classification task using KNN and PCA to pre-process the data. The dataset contains 101 continuous variables and the column of the labels (here the link to download the data filebin....
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22 views

How does caret resolve ties in the KNN classification? [closed]

I have a multi-class classification problem, in which I'm using caret package k nearest neighbour classifier, (4 classes), which means that an odd number for k won't prevent classification ties. So ...
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k-means/k-nearest neighbours on multi-dimensional scaled data

I used the Python manifold library for multi-dimensional scaling on my distance matrix. Can I use k-means or k-nearest neighbours on ...
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How to select the most important features, categorical & numerical data

I need to find out which factors are relevant when predicting low birth weight. My model looks like this: ...
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Calcule in-knn or out-knn (in-degree of nearest neighbours) of network. Assortativity in- out-degree and in- out-knn

I am trying to calcule using igraph the in-degree of nearest neighbours (in-knn) of a network or out-degree of nearest neighbours (out-knn). How can I do it in igraph (R)? This question is to ...
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How valid is this Stacking Model (input features to weak learners are different)?

I have a set of features with 6 of them being categorical, 1 continuous and 2 textual in type. I have to predict the labels ( 10 in number) for them. I tried applying several models and came to a ...
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29 views

What units is my mean squared error if I center and scale my training data?

I have a KNN model that I used to predict the close price on houses. ...
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How do I avoid time leakage in my KNN model?

I am building a KNN model to predict housing prices. I'll go through my data and my model and then my problem. Data - ...
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What does the intercept represent in a model matrix?

I am making a KNN algorithm to predict close_price with about 80,000 rows of this data. ...
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Different k’s for KNN

I would like to see the knn model performance on my data for various values of k. Can I just take the same training data and compute on it the knn for different k values or should I do cross ...
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Determining epsilon for DBSCAN

I'm using the method described in this paper for determining the optimal epsilon value for DBSCAN clustering in which a plot of the nearest neighbors is used: However, the plots in the paper and ...
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How to choose the right forecast method for variable 'X' when I have some available forecast for variable 'Y' with historical data of X and Y?

I have yearly historical data for variables 'X' and 'Y'. Say the time frame is 't'. In addition to available historical data, I also have the forecast data of variable 'Y' for t+1. My aim is to ...
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Query complexity of ANN based on Hamming distance

Ilya R provides the following query complexity of ANN for Hamming distance based on coordinate sampling https://www.ilyaraz.org/static/class_2018/files/20181023.pdf When he says ...
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Knn Classification on iris dataset

I'm following along https://rpubs.com/Drmadhu/IRISclassification to understand Knn classification. Here's the code I have: library(FNN) iris.sample<-sample.int(n=nrow(irisdat),size=floor(0.75*...
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Construction of confusion matrix when cross-validating with k-NN in R

I've a dataset looking like this: ...
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Estimation of average treatment effect based on nearest neighbor matching [closed]

I would like to use R to duplicate the treatment effect estimation method used in Stata. Specifically, this is the Stata method I would like to duplicate. I have tried the package ...
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Why is “condensed nearest neighbour” Parametric? [duplicate]

Definition of "condensed nearest neighbour", at training time it chooses the c "best" training examples (where c is a hyper-parameter), and at test time uses the usual KNN prediction but based only on ...
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Why is “consistent nearest neighbour” Non-parametric? [duplicate]

Definition of "Consistent nearest neighbour", runs our usual KNN classifier but instead of viewing k as a hyper-parameter it always sets k = ceil[log(n)]. So far, I looked-up many references and ...
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Data leakage in multilevel validation

I participate in competition that have historical data. I break it down according to this scheme. ...
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31 views

Optical Character Recognition - digits on the screen

My task is to classify a digit based on a small image containing one digit only. The font type and size is the same across the training/test dataset, but the position of the digit in the image might ...
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k-Nearest Neighbors multiclass - what if we can't form the majority?

I'm watching a lecture on applying kNN to the digit recognition (MNIST dataset) problem. Let's say $n$ denotes the number of neighbors. This is a multiclass classification problem, so having an odd ...
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How does model accuracy compare across the folds in cross validation

If we have two cross validation from KNN and linear regression like this: KNN: ...
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Loss function for KNN Regressor

What is the Loss function for KNN Regressor? Would it be similar to OLS? If so what would be the main difference?
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Predict Click Through Rates for Google depending on the Position

I'm given the task to calculate expected click through rates (CTR) for rankings of a given site in Google, using the data Google provides in their Google Search Console. What I get there is basically ...
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Hypothetically, can we perfectly classify new instances given infinite support from validation and training? KNN

Hypothetically speaking, Given infinite amounts of training data and validation data, can we achieve perfect classification (score of 1) given ML algorithms such as KNN? Thank you.
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Dimensionality Reduction for Optimally Preserving KNN

Do any dimensionality reduction techniques find embeddings which optimally preserve the K-nearest neighbors of each point? If no algorithm provably does this, are there algorithms which heuristically ...
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Training data grow with K-NN and Naive Bayes

My doubt is about the grow of the training data using K-NNs and Naive Bayes. As it grows larger, does prediction (on test data) become also computationally harder?
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Is this an example of overfitting?

I am trying to predict some future values using either KNN or regression model. I have about 9 independent variables that do not seem to have strong correlation to each other (Not completely sure ...
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A simple concept question regarding Bootstrapping

I am having a difficulty understanding whether I can use the bootstrapping method for prediction. First off, my data is as follows. where personal Income is the dependent variable (y), and GPA, Age, ...
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k nearest neighbor for likert data

I have a data set which contains several variables. All these variables are catgorical variables. I want to use k nearst neighbour method. When I am using this technique do I need to convert the data ...
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How to evaluate the goodness of a KNN-based recommender system?

I'm building a content-based recommender system and I'm using KNN. That means, for each test instance I'm using KNN to find the most similar objects, and then come up with a recommendation. However I'...
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64 views

Propensity score matching: bias adjustment

I'm using propensity score matching to match similar individuals. I.e., I first estimate a propensity score (the probability of treatment conditional on some set of variables) and then match on the ...
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Quantifying a manifold folding unto itself

I have a dataset of ~7k scattered points in 3D which represents a manifold that may or may not "fold unto itself". Here's an example where this does happen (look at the top-right yellow triangles): ...
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What if the best k in k-NN is equal to the number of data points?

I need to train a regressor (in the machine learning sense). I have tried many different methods and so far nothing works better than just a constant prediction. In other words in looked like I have ...
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Citation for selection of weighted KNN weights via cross validation

Examples of choosing the hyperparameter $K$ for the $K$-nearest neighbors algorithm via cross-validation abound in the literature. As with most `local' methods, a relatively trivial generalization of ...
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Probability density function of a data point given the locations of its four nearest neighbors

The goal is to find the probability density function $p(\mathbf{x} | \mathbf{c}_1,\mathbf{c}_2,\mathbf{c}_3,\mathbf{c}_4)$. Here $\mathbf{x},\mathbf{c}_i \in \mathbb{R}^d$. $\mathbf{c}_1,\mathbf{c}_2,\...
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K-NN optimal value of 'K'

How can I find the optimal value of 'K' in K-NN using the cross_val_score function, with scoring metric as auc_score? Do I need ...
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1answer
134 views

KNN regression: Why does my In sample RMSE look like my out of sample RMSE across K values?

I'm expecting the RMSE plot for my KNN regression model to look like the above image but I'm getting the below when running my code hosted here. Any ideas on what could cause this? I believe something ...
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Some K-nearest neighbour predictions do not make sense

Main problem: Why are the predicted rent of customers with a certain level of income higher than rent of customers with both higher and lower levels of income keeping other variables fixed? And how ...
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Predicting response on boundaries of observations with KNN

Suppose a bivariate process is observed $(X_1, Y_1), \ldots, (X_n, Y_n)$ that follows the probability model of ordinary linear regression $$Y_i = \alpha + \beta X_i + \epsilon_i, $$ and assume $\...
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Naive Bayes and kNN accuracy

Assume that a large number of binary features are added to a dataset with two class labels c1 and c2, such that for each added feature f, the class conditional probability P(f = 0|c1) = P(f = 0|c2). ...
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how to prevent overfitting with knn

Using too low a value of K gives over fitting. But how is overfitting prevented: How do we make sure K is not too low And are there any other precautions taken in k-nn that help prevent over ...