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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Python SK-Learn KNN Imputer ( "ValueError: could not convert string to float: )
I have data with missing values.
All columns are integer, except for a column that has missing values.
These missing values, were set with a "?" which was converted to NaN using the Numpy ...
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Finding Accuracy, Recall, Precision, and F1 from Matlab Confusion Matrix
I'm working on a project to find the highest accuracy between KNN and a Decision Tree for Classification using Matlab.
How to calculate the Accuracy, Recall, Precision, and F1 from the output below? ...
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Top hundred nearest neighbour
I have a dataframe with a column called pharmacy number and other columns corresponding to each pharmacy number, there many rows and each row corresponds to pharmacy number. I want to create a ...
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Semantic search with pretrained BERT models giving irrelevant results with high similarity
I'm trying to create a Semantic search system and have experimented with multiple pretrained models from the SentenceTransformers library: LaBSE, MS-MARCO etc. The system is working well in returning ...
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K nearest neighbor with varying feature length
I'm trying to build a tool which predicts the elemental composition of some light source using its emission spectrum with the k nearest neighbor algorithm (I'm using the KNeighborsClassifier from ...
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Rejoin Categorical Labels After Running Cluster Analysis
I'm working on creating a kNN & PCA analysis using 6 variables associated with different schools.
The first step is to drop the school name label column which the first column, so only numeric ...
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How to implement Ant Lion Optimization (ALO) feature selection for KNN Classification Problems?
I have been assigned for a project related to text data classification, i have preprocess and vectorized the data with TF-IDF. For feature selection i am using pyMetahueristic library to implement ALO ...
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KNN Accuracy training
After I developed my model using KNN I get the following accuracy:
Train Accuracy :: 1
Test Accuracy :: 0.24
What is the accuracy of my model?
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Where can I find implementation of the various improvements of K-nearest neighbors (KNN)?
I have been facing some challenges where traditional KNN algorithm perform well. I'd like to explore more advanced knn solutions. While researching possible solutions, I came across a paper titled
<...
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How to add query filter to the Nearest Neighbors algorithm?
I have Nearest Neighbors model, built with sklearn sklearn.neighbors.NearestNeighbors, which I use to make content based recommendations.
Sometimes I need to ...
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Evaluation parameter in knn
I am using KNN for a regression task
It's like that:
1- I normalized the data
2- I calculated the distance of the new data with the previous data (Euclidean distance)
3 - I choose k nearest neighbors
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estimating coordinate correction
I'm working with 3d coordinate data (x,y,z), however I know that the z coordinate is systematically wrong and the error of z is dependant on both x and y. I however do have some data where I know the ...
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Get multiple predictions from a knn model
I want to my code to return multiple(5) predictions from my trained knn model. I've tried using predict_proba() but it just returns the probabilities and not the names
Here is my code:
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Is it sensible to use the ROC curve with an KNN model? And if so why?
I am a beginner doing my first ML project. I am doing a binary supervised classification on an unbalanced dataset and want to use the ROC curve as a performance metric of my models. I am using ...
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Precision and AUROC for which class values
I am a newbie in reading research paper and implementing it by myself. I went through the paper Breast Cancer Survival Prediction from Imbalanced Dataset with Machine Learning Algorithms.
Can anyone ...
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KNN using Mahalanobis distance gives low score [closed]
I want to get average score of all possible K but the average accuracy I'm getting is much lower than what's given to me.
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What does "n∼1/d" mean?
Or rather, what is the "~" notation mean? For reference, I'm trying to understand this section from a sci-kit learn documentation page.
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Near duplicate detection algorithms for a near real time system
I'm looking for near-duplicate detection algorithms or techniques for a near-real-time system with large document volumes. I know LSH is the most popular industry-standard algorithm for syntactical ...
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Algorithm for optimally removing items for improving a nearest neighbors embedding
Let's say that you embed a collection of items belonging to multiple classes into a multidimensional space for predicting unseen future items by K Nearest Neighbors.
And in a particular scenario it is ...
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KNN improvements (python)
I rencently had to work on a problem where the best baseline was knn (geolocalised data). I have different targets (binary classification, multiclass classification and regression) and associated ...
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What does a leaf size of 1 in K-neighbors regression mean?
I am doing hyperparameter tuning + cross validation and I'm constantly getting that the optimal size of the leaf should be 1. Should I worry? Is this a sign of overfitting?
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parallel work on KNN in python
I have a question, related to parallel work on python
How I can use Processers =1,2,3... on k nearest neighbor algorithm when K=1, 2, 3,.. to find the change in time spent, speedup, and efficiency.
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ROC_AUC score is higher before tuning n _neighbors for KNN
This is for multiclass classification. Before tuning the n_neighbors for KNN, these were the results:
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Best way to vectorise names and addresses for similarity searching?
I have a large dataset of around 9 million people with names and addresses. Given quirks of the process used to get the data it is highly likely that a person is in the dataset more than once, with ...
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Nearest neighbor face recognition in eigenspace when using dot product of test set with eigenvectors does not match the performance when using sklearn
I am trying to perform Face recognition using PCA (eigenfaces). I have a set of N training images (of dimensions M=wxh), which I have pre-processed into a vertical ...
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Recommend different product using NearestNeighbour
I am working on creating a recommendation system which suggests product for the user, based on the other user's data from the same region.
My dataset is as below
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How does Scikit learn KNN handle categorical input variables?
In some articles, it's said knn uses hamming distance for one-hot encoded categorical variables. Does the scikit learn implementation of knn follow the same way.
Also are there any other ways to ...
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Need for cross-validation in KNN
I read that we need cross-validation in KNN algorithm as the K value that we have found from the TRAIN-TEST of KNN might not be generalizable on unseen data.
The logic given was that, the TEST data ...
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Why feature normalization can increase the biometric recognition accuracy?
In a biometric recognition system, I have noticed that normalizing the extracted wavelet features leads to increasing the recognition accuracy. The classifier used is K-nearest neighbor (KNN), and ...
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Massive difference in accuracy of KNN depending on random_state
pardon the noob question but I am baffled by the following behavior. My model has MASSIVELY different results based on the random seed. I want to train a KNN classifier on the famous Kaggle Titanic ...
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Trim left tail of music in audio file
I have audio files, most of them start with the same music, and then a conversation begins. I want to trim the part of the music (which can be varied in length). I have no labels, I can transcribe the ...
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How can I know that this works and is correctly done?
Problem: This seems to be very wrong, cannot pinpoint what I am missing or doing wrong here. Should all rows for each column be Normalized? How?
I am working on some small thing, and want to get a ...
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Categorical Variables (Y or N) in KNN Classification?
Practicing KNN and I just had a query about pre-processing, as I understand KNN doesn't work with categorical features.
I've read into one-hot-encoding (dummy variables) which I suppose if I applied ...
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Balanced target variable in KNN classification
In many places, I have seen it only mentioned that predict the label of the query point as the label with more than half of the labels of it's K nearest neighbours. However, I don't see it mentioned ...
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KNN model with two classes in the train dataset and three in classes in test
I have a dataset like this:
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How different classifiers would perform on a particular data set
I am reading through and learning how different ML methods work on different types of data, but I have faced a data set that I am not sure how ML methods, such as decision tree, Naive Bayes, and KNN, ...
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How to determine the number of K in KNN
I have a question about how many k values (k=1 or k=5 or k=50) to choose in the following two scenarios. I initially thought choosing k=5 will be the right choice of k for both because it will ...
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New classification in Machine Learning KNN model
This is my example of KNN model (I write it using R):
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Comparison of classifier confusion matrices
I tried implementing Logistic regression, Linear Discriminant Analysis and KNN for the smarket dataset provided in "An Introduction to Statistical Learning" in python.
Logistic Regression ...
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What is the principial difference between zero-shot learning and k-NN and clusterization based methods?
One can consider clustering and k-NN to be a zero-shot, too?
I think there is no much principal difference, except using some neural network architecture (usually it is a transformer) which is used to ...
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How to find the right number for a training set for machine learning
I would like to develop a machine learning algorithm using the knn model to perform a classification of my data records. My question is: is there a general method to follow to determine how large my ...
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Taking the squared Euclidean distance for kNN classification of images
A problem I'm working on states:
Computes the squared Euclidean distance between each element of the training
set and each element of the test set. Images should be flattened and treated
as vectors.
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Is creating decision surface necessary in k-NN?
I am new to machine learning and I came across this question.
*1) [True or False]
k-NN algorithm does more computation on test time rather than train time.
Solution: A
The training phase of the ...
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K-NN algorithm with maximum distance to be considered a neighbor
Is there a variant of the k-NN algorithm where the label returned is:
the average of values of the k nearest neighbors that are closer than a given threshold to the query data point?
no value if ...
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Algorithms for SMS spam detection
Which among KNN, Logistic and Naive Bayes would yield best results for SMS spam
detection? Is there any other efficient approach worth exploring.
I am planning to make a python application for SMS ...
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Does knn extend the train dataset by test values during the prediction?
Lets say I have 100 values in my dataset and split it 80% train 20% test. When predicting the last value, is the prediction based on previous 99 (80 test + 19 already predicted values) or only the ...
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If we dont specify any distance in KNN model, how is n_neighbors parameter calculated?
If we don’t specify the distance, how is the n_neighbors calculated?
