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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19 views

Why is KNN better at K-Fold Cross Validation than XGBoost or Random Forest?

I've been running K-Fold cross validation multiple times for KNN, random forest and XGBoost. KNN can complete sklearn's cross_val_score, so much faster consistently. They all use the same preprocessed ...
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1answer
17 views

Chose most similar sample (mutivariat) [closed]

I have a list of 50-100 samples with 4-8 Variables (see below). I am trying to find a suitable algorithm to select the "most similiar" sample to a new sample that comes in (given parameters ...
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1answer
69 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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61 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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10 views

Calculate euclidean distances for KNN and cross validation given a 99x16 10 folds

I'm trying to implement KNN classification with cross-validation implementation in python. The data consists of 10 folds of size 99x64, each with their corresponding label of size 99x1. Do I have to ...
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1answer
22 views

KNN efficient implementation

The KNN algorithm is very handy and particularly suited to some of my problems, but I can't find any resources on how to implement it in production. As a comparative example, when I use a neural ...
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31 views

kNN for non-ordinal variables

kNN is a distance-based method, so it requires the input to be in numerical form. I was wondering if it is possible to use kNN imputer for non-ordinal categorical variables (like color). Since the ...
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25 views

In KNN, k is the minimum number of neighbors that a point must have that share the class label?

Someone more knowledgeable than me is telling me that the title contains the definition of K in KNN. I understood K as the number of nearest neighbors to consider and that the majority class label ...
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1answer
52 views

Recommendations based on other products seen

I am trying to develop a basic book recommender system to get in touch with the field and start learning methods and how to prepare the data. The Dataframe I am using is pretty plain, it has the ...
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1answer
37 views

train_test_split() Error: cannot reshape array of size 900000 into shape (100,100,3)

I am fairly new to python and I have a program for data classification using the k-nearest neighbor method. But I encountered an error when running the program. Here my source code: ...
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1answer
118 views

Question about reshaping array size for KNN Classifiers

I keep trying to run a new set of data through my KNN Classifier but would recieve the message: ValueError: query data dimension must match training data dimension ...
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11 views

Difference between KNN-DTW and DTW Template matching?

Is there a distinction between kNN-DTW and DTW template matching? Essentially DTW template matching computes the DTW for each point in the training set, similar to the KNN-DTW algorithm. Is there a ...
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10 views

DTW and kNN-DTW time complexity

I have implemented KNN using a custom DTW metric with sci-kit learn and as shown below: ...
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2answers
50 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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3answers
602 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

When using KNN, how do I know which order of the Minkowski distance to use?

I am learning about KNN and ML in general. I know that KNN usually uses second order Minkowski distance (Eucledian Distance), but I assume it cal also use other orders. But what is the benefit to ...
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51 views

Is it valid to add MAPE as a margin to prediction output?

I've trained a KNNRegressor on predicting used car prices. A given car's actual selling price is R289,995. My model predicts R260,911. I want to be able to tell the user My knn model predicts the deal ...
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1answer
426 views

k-Nearest Neighbours with time series data - how to obtain whole-time-period estimators

I have a large dataset for the activities performed by multiple staff in a factory over a long period of time - 01/01/2017 - present. The activities performed by different staff are recorded at each ...
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1answer
50 views

K-Nearest neighbor in transformed space

When googling "weighted KNN", the results appear to be focused on weighting the nearest neighbor values after those neighbors have been determined. I'm looking for something that assigns a level of ...
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2answers
78 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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15 views

KNN with high-variance data [closed]

KNN doesn't work well with high-variance data, so how should I fit my data? Here is an example of what the data looks like:
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2answers
78 views

sklearn KNN fit throws out error : value too large for dtype('float64')

I have cleaned the data from nan values and infinite values, the only feature which has a large float is the column 8 (it's a sum) I have no Idea how to fix this last error, I tried all previous ...
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2answers
138 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 ...
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1answer
32 views

Which algorithm should I choose and why?

My friend was reading a textbook and had this question: Suppose that you observe $(X_1,Y_1),...,(X_{100}Y_{100})$, which you assume to be i.i.d. copies of a random pair $(X,Y)$ taking values in $\...
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2answers
61 views

Understanding Learning Curves

I would like to clarify my understanding of learning curves with two example plots below. I am experimenting with small data sets here between 500 and 1500 samples to clarify my understanding. My ...
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0answers
11 views

Is it possible to use kNN with streaming data

I have built a kNN model using scikit learn that is able to predict a binary outcome very well. The data itself is quite basic, it is simply a 1-D waveform. When feeding the waveform into the model ...
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1answer
101 views

Random Forest vs K Nearest Neighbor as non linear classifier [closed]

When classes are non-linearly separable, which of the following methods performs better? Choose correct one :- Logistic Regression Random Forest K Nearest Neighbor Classification Linear Regression ...
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9 views

what is store in leaf node and split node in Annoy or any approximate nearest neighbor model built using tree?

Im trying to understand the working of annoy and have read the code _make_tree since im not from C++ background im trying hard to figure out the logic of whats stored in leaf node and split node ,you ...
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1answer
174 views

KNN Imputation utilize mean or mode?

In my current project, I am doing KNN imputation with K = 5 and I am using sklearn.impute.KNNImputer. I have a mix of continuous and nominal variables(encoded as 0/1 or ordinal ones that have been ...
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18 views

Does Sklearn's KNeighborsClassifier Map Input to Output If Dimensions Don't Match?

I want to classify a hyperspectral image (Indian Pines data set). The input is of shape (145, 145, 200) = a HSI of 145x145 px with 200 bands. Each one of the 145x145 pixels should be classified to one ...
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3answers
76 views

Optimal selection of k in K-NN

I am currently reviewing some concepts related to Machine Learning, and I started to wonder about the hyperparameter selection of K-NN classifier. Suppose you need to solve a classification task with ...
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2answers
35 views

Missing value Imputation in dataset

I have two separate files for Testing and Training. In the training data, I am dropping rows that contain too many missing values . But , In the test data , I cannot afford to drop the rows so I have ...
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1answer
93 views

adding a supervising process during knn process

I am trying to improve my KNN regression process (I would like to use sklearn / python, but it doesn't matter).I would like to improve my results and to gain insight. Here is an example: I have data ...
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1answer
38 views

Does shuffling the training data matter in a K Nearest Neighbors Classifier model?

I am new to machine learning and I have a couple of questions about a project. So, I created a classifier using the MNIST data set for a ML project that I was working on. I augmented the data by ...
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2answers
57 views

Using KNN to categorise inventory (physical stock items) - is it the best way?

I'm working on a machine learning problem involving inventory (i.e. physical retail stock), however through the cleaning (outlier removal) process some of the items (via their corresponding ...
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14 views

Same accuracy on KNN for different distance metrics

I am new to data analysis and I am trying to run a kNN classifier on a lung cancer dataset with multiple attributes. For all k values I tasted (1 to 10), I obtain the same accuracy when using either ...
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1answer
101 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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63 views

Estimate eps value in DBSCAN using KNN algorithm

I would like to estimate the best eps value for the DBSCAN algorithm on this dataset by following this set of rules: Set a minPts: 10 Compute the reachability distance of the 10-th nearest neighbour ...
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2answers
23 views

Impossible to increase model accuracy [closed]

I'm building binary classification models on my company's dataset. The problem I'm having is that I haven't been able to increase the accuracy of my models. I have trained, tuned, cross validated ...
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3answers
9k views

Features selection in KNN

I have a naive question about using the K Nearest Neighbor algorithm: is feature selection more important in KNN than in other algorithms? If a particular feature is not predictive in a neural ...
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1answer
82 views

Combine K-nearest neighbor with categorical embedding

I've tried a few ways to do my multi-class classification. For categorical data, I used the embedding technique with Tensorflow, which moves the entity closer with its similarity. This technique ...
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1answer
49 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
575 views

Learning with Positive labels only

I have ~7 million rows of customer data (~500 sparse attributes) A million out of them have opted in to a new service. How do I use this signal to predict which of the remaining customers are likely ...
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1answer
34 views

Standardization on training and split data

I am confused on which of the following should be used for standardization: method 1: fit transforming training data and only transforming test data ...
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1answer
46 views

Why does the overfitting decreases if we choose K to be large in K-nearest neighbors?

I am studying machine learning and I am focusing on K-nearest neighbors . I have understood the algorithm, but I have still a doubt, which is on how to choose the K for the number of neighbors. I ...
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0answers
14 views

Redo preprocess on unknown row

I'm trying to write a script to get the most similar rows in a certain dataframe, based on a single row. Using scikit-learn The method I need is ...
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0answers
54 views

Classification and clustering of Time series data of temperature

I have a time series recorded data of temperature. This is what my data looks like: The change in data represents specific event or a class which I would like to detect when new incoming data. ...
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1answer
71 views

KNN Regression: Distance function and/or vector representation for datetime features

Context: Trying to forecast some sort of consumption value (e.g. water) using datetime features and exogenous variables (like temperature). Take some datetime features like week days (...