Questions tagged [k-nn]

K-Nearest Neighbor (K-NN) is a classification algorithm that determines the label of some data point based on the most common label of the closest k other points.

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1answer
26 views

find most dense neighborhood of points in high dimensional space

I'm working on a project where I have many high-dimensional points and I want to find the most dense neighborhood of them. Ideally, out of my ~500 points that are each a 4x300 matrix (300 ms time ...
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1answer
38 views

How are images (roughly) clustered when using KNN

When classifying data by KNN, the classes are calculated by using the distance between datapoints. For example, the case of real estate, where the x-axis is the price and the y-axis the size of real ...
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14 views

euclidean distance between true value and estimated value as input for knn [closed]

If we apply knn on euclidean distance between true value and estimated value will the results be better in case of data classification?
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1answer
25 views

How can I compare my regressors?

I am trying to build a regressor for a dataset which gives info about students' school performance and the probability of getting admitted in the University of their choice. The first 5 observations ...
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1answer
45 views

KNN custom transformer shows same accuracy for every k i set

I built custom trasformer for KNN and i can't figure why my k-number, when i set it, always shows same accuracy... ...
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2answers
30 views

KNN with mixed data (feature set)

I have a dataset where the feature set consists of hour of the day (between 0 to 23), day of the week (Monday to Sunday), number of shops (a positive integer) and road category (0 to 8 on an ordinal ...
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1answer
26 views

Which kNN model to chose?

I am trying to tune the "n_neighbors" for a kNN model andI have the following problem : Based on the mean cross validation score the optimal kNN model should be the one with 10 neighbors. On the ...
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1answer
44 views

Does this line in Python indicate that KNN is weighted?

Does this line in Python indicate that KNN is weighted? clf = KNeighborsClassifier(n_neighbors=5, metric='euclidean', weights='distance') Are the weights the ...
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0answers
12 views

Combining several Multi-Output-Models into a single Multi-Output-Model

I'm trying to create a k-Nearest-Neighbor based model of 76-dimensional input data $I$ and 44-dimensional output data $O$. Through domain knowledge I know that only certain input dimensions are ...
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1answer
20 views

Converting Text Entries into values for machine learning - KNN

Consider the following dataset. ...
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1answer
36 views

Maybe wrong values for precision and recall

I'm trying to do some data mining with RapidMiner studio. I've applied the K-nearest neighbor algorithm with different values of K. As I expected, accuracy increase and after K=5, it decrease. But I ...
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47 views

Operands Could not be Broadcast with Shapes (19,)(0,)

I have googled and read something similar to the problem I have but I do not seem to know how to fix the error I got from this particular code: ...
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1answer
42 views

Making Sense of this Error Message

I am using a book and a video to learn how to use KNN method to classify movies according to their genres.This is my code: ...
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1answer
116 views

Is Annoy a machine learning algorithm to find nearest neighbor ? and is it similar to K nearest neighbor algorithm?

I was researching about Google universal sentence encoding and i saw that it uses simple neighbor/Annoy to find the nearest vector for semantic-similarity search engine. This is the first time i'm ...
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10 views

How to quickly find min, max and mean distance between samples fitted in Nearest Neighbors?

I am familiar only with SciKit Learn implementation of Nearest Neighbors model where one can fit data and execute K-neighbors or radius queries. This model does not provide any statistical information ...
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1answer
31 views

KNN scoring low compared to Logistic regression in MNIST challenge

KNN gives me a score of 0.76100 while it shows 94% accuracy for my training data (splitted with test_size =0.3) in my jupyter notebook while logistic regression gives me a score of 0.91485 with an ...
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2answers
145 views

kNN vs Logistic Regression

Good day, I had this question set as optional homework and wanted to ask for some input. Suppose an individual was to take a data set, divide it in half into training and test data sets and then ...
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0answers
24 views

Feature importance in SVM

Why is there no command for feature importance in SVM like the one provided in Random Forest feature_importance_ from ...
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0answers
48 views

How to evaluate unsupervised KNN?

I'm creating a recommender system using an unsupervised nearest neighbors model to suggest similar publishers for a given publisher, advertiser combination. I'm wondering how to evaluate the model I ...
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0answers
23 views

Could some explain how to determine the boundary of the root cell of a k-d tree with a concrete example, such as knn on iris dataset?

KNN uses k-d tree to calculate the nearest neighbor(s). Wiki gives this figure (A 3-dimensional k-d tree) to illustrate the process. question How to determine the boundary of the root cell of a k-d ...
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2answers
36 views

Varying the number of neighbors in nearest-neighbors regression

For nearest-neighbors regression, it is plausible to increase the number of neighbors used to predict $f(x)$ when there are many data points near $x$. What is a good algorithm for varying the number ...
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1answer
27 views

Has this paper used weighted KNN or not?

Please tell me if you see this paper in the link below has used weighted KNN? because they have used weights as the training and testing samples and no formula written. They don't explain the ...
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1answer
61 views

Ball Tree and Pseudometrics

The docs for sklearn.neighbors.DistanceMetric state that in order to be used within the BallTree, the distance must be a true metric (i.e. be non-negative, 0 ...
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0answers
51 views

What does big O mean in KNN optimal weights?

Wiki gives this definition of KNN In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In both cases, the input ...
2
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1answer
111 views

values passed to user-defined distance function by KNeighborsClassifier is wrong

I have a data-set in which all features are binary and the class of each data-point is also binary. I am trying to use KNearestClassifier with a user-defined distance function as follows: ...
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1answer
110 views

How to predict the value in KNN?

I am trying to build the KNN algorithm for IRIS dataset. First, I've computed the distance and stored it in 1d array. However, I am really struggling to build the prediction function. Therefore two ...
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1answer
42 views

Is the distance in Nearest Neighbors a good measure of similarity?

Let's train a Nearest Neighbor model with just one sample in it: In [48]: nn = NearestNeighbors().fit([[0, 1, 0, 0]]) So this one sample has just one significant ...
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1answer
49 views

Machine Learning algorithm for detecting anomalies in large sets of events

Let's start with the following hypothetical preconditions: There is traffic: normal and anomaly. Each traffic sample contains a list of events (of variable size) Events happen in order, the possible ...
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1answer
43 views

Item-based recommender using K-NN

I'm trying to build an item-based recommender using k-nn. I have a list of items, all of which have some properties (features) in common. ...
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1answer
54 views

What is the Value of X in KNN and Why? [duplicate]

I have a dataset of 25 instances these instances are divided into 2 classes Green Circles and Blue Squares data distributed as this graph I want to predict X's class based on "Likelihood Weighted ...
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1answer
186 views

logic behind weighted KNN

I am reading about KNN So I made another example to make things clearer In this example (Image attached) You can see there are in total 5 Greed Circles and 20 Blue Squares by standard KNN (k=3) , ...
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1answer
79 views

adding a supervizing process during knn process

I try to improve my knn regression process (I use sklearn / python, but it doesn't matter). Because I can have a scientific point of view on my data, I would like to improve my results. I give you an ...
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2answers
40 views

Reverse engineering a distance metric from the output of a k-NN

Suppose that someone has trained a nearest-neighbor algorithm based on some unknown metric. I have a large dataset of $N$ observations and $P$ features. For each observation, I am given $K$ indices ...
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1answer
102 views

KNN RandomizedSearchCV typerror

While trying to study a binary classification problem with KNN and trying to tune the parameters of the model I'm getting a typerror that I quite don't understand. Is a parameter missing or something? ...
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1answer
48 views

Mapping of categorical features into binary indicator features

I am currently reading an introductory machine learning book by Daumé (ch. 03, p. 30) and when discussing the mapping of categorical features with "n" possible values into "n" binary indicator ...
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1answer
23 views

Does it make sense to use KNN to target ticket buyers for arts events?

I work for a medium-sized non-profit. We have a database with approximately 40,000 profiles of folks (mostly patrons). For each show a patron attends, we add an attribute that indicated that they're ...
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4answers
1k views

How to save a knn model?

I need to save the results of a fit of the SKlearn NearestNeighbors model: knn = NearestNeighbors(10) knn.fit(my_data) How do you save to disk the traied ...
2
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1answer
97 views

Coordinate System's influence on $L$ distances (Manhattan and Euclidean)

I don't understand this picture, which says if we change the coordinate system, we would have the same result for $L_2$ distance, whereas, our result would differ for $L_1$ distance. What does it ...
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0answers
50 views

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 - ...
2
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1answer
78 views

K-Nearest Neighbours algorithm explanation needed

I need some explanation for K-Nearest Neighbors algorithm. Why is the training process needed in KNN algorithm? In regression models the training process means to find optimum parameters for a ...
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1answer
42 views

Normalization(minmax) gives me worse results than before in KNN, follow up actions?

Hello I'm studying a classification problem with KNN right now. I have many numeric features that I normalized with MinMaxScaler, I also got some OHE categorical features that not seem to cause the ...
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1answer
47 views

Can `k=1` be a good choice for K neighbors classification?

Running sklearn.KNeighborsClassifier() on Kaggle's Leaf Classification sample (set of 99 species, 10 specimen each), with defaults kNN parameters and a grid search ...
3
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1answer
330 views

K Nearest Neighbour with different distance matrix to each datapoint

I'm wondering if there is library support in python (such as sklearn) for doing KNN on a data set that has a custom distance matrix (positive definite) for each data point (x is a query point, $x_i$ ...
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0answers
52 views

Is there an algorithm that imputes missing values based on n nearest columns? (KNN hybrid)

I have a dataset of 70 columns that have missing values. Each column has a few columns (3-5) that it is significantly more correlated than the others but each column's correlated columns are very ...
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2answers
251 views

Choosing k value in KNN classifier?

I'm working on classification problem and decided to use KNN classifier for the problem. so if k=131 gave me auc of 0.689 and k=71 gave me auc of 0.682 what should be my ideal k? Does choosing ...
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1answer
29 views

Date transformation for KNN to get distance [duplicate]

I have data set with date features like 01/01/2019 and I would like to use KNN. However, I cannot find a good transformation for dates that has a meaningful distance result for the last feature. For ...
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2answers
79 views

Date transformation for KNN

I have data set with date features like 01/01/2019 and I would like to use KNN. However, I cannot find a good transformation for dates that has a meaningful ...
7
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1answer
132 views

How to decide how many n_neighbors to consider while implementing LocalOutlierFactor?

I have a data set with rows: 134000 and columns: 200. I am trying to identify the outliers in data set using LocalOutlierFactor from scikit-learn. Although I ...
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0answers
307 views

Training anomaly detection on text string [closed]

I have text string stored in a column, resulting after pre-processing of web traffic data. Now I want to apply anomaly detection on it and for that purpose I applied ...
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0answers
20 views

Neighbourhood based approach on implicit feedback datasets

Is it possible to use a neighbourhood-based approach on implicit feedback dataset? I have a dataset with information about how many times a certain item was viewed by a particular user. I know that MF ...