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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How to handle date data for Knn?
I'm working on a project about predicting kickstarter project success(classification) and my dataset has many columns that could be used as features such as :
state_changed_at,
launched_at,
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How Does Weighted KNN Work?
I am reading notes on using weights for KNN and I came across an example that I don't really understand.
Suppose we have K = 7 and we obtain the following:
Decision set = {A, A, A, A, B, B, B}
If ...
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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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How can I implement tangent distance for k-nearest neighbor in python/scikit-learn?
My ultimate aim is to have a function which I can feed into scikit-learn's NearestNeighbor class as a custom metric parameter.
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How does KNN work if there are duplicates?
I am currently debating with my friend about how KNN handles duplicates. Suppose K = 2, and we have a 1-dimensional set of data points to illustrate my dilemma
I = {1, 2, 2, 2, 2, 2, 6}
Thus is it ...
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Is there any way of ordering/sorting vectors?
I am working on KD-Tree for nearest neighbor algorithm, where at each level of the tree we arbitrarily choose a dimension to cut upon and sort the points based on that chosen dimension value, after ...
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Best way to find dissimilarity in a 6x2 DataFrame?
I'm new to data science and am currently learning different techniques that I can do with Python. Currently, I'm trying it out with Spotify's API for my own playlists.
The goal is to find the most ...
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Is there an upper bound for k in nearest neighbors-based methods?
When applying a nearest neighbors-based method to a data of, for instance, 2000 points, what is the largest number of neighbors that can be considered ?
I am using a nearest neighbors method in an ...
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Creating a single number from a numpy array - Python [closed]
I am working on a gender classification project. I am extracting the pixels of an image using a Numpy array in Python, similar to the one below:
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Problem with calculating error rate for KNN
I am trying to validate the accuracy of my KNN algorithm for the movie rating prediction.
I have $2$ vectors: $Y$ - with the real ratings, $Y'$ - with predicted ones.
When I calculate Standard ...
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Why isn't local averaging (including KNN) used often for regression?
My professor said that the "holy grail of regression" is the function E(Y|X=x) i.e. the conditional expectation of Y on X. In practice, you'd take a small window of X and take the average value of Y ...
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How to decide the shape of input features, when each data file is of different length?
To help me understand the benefits and shortcomings of decision trees, KNN, Neural Networks, ...
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Taking Neural Network's false positives as the recommendation system result?
I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 ...
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How to determine the k in kNN [duplicate]
I'm looking to use the k-Nearest Neighbors (kNN) algorithm. What are the possible methods for determining the best K? From what I have read, looking at many different values(say 10-100) should work, ...
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Is K-NN applicable for binary variables?
I need help because I'm just new to machine learning and I do not know if k-nearest neighbors algorithm can be used to identify the appropriate program(s) for Student 11 in the table below.
The ...
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scalable tools to build kNN graph over sparse data
I'm looking for scalable tools to build kNN graph over sparse data points.
The dimension and number of data points can be both up to millions.
What I have tried already:
sklearn.neighbors....
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Python-SQL database [closed]
There are two SQL databases in which one database contains user data and other database contains service data. Firstly, I am connecting the databases and fetching it on python.Based on the user data, ...
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Sklearn: unsupervised knn vs k-means
Sklearn has an unsupervised version of knn and also it provides an implementation of k-means.
If I am right, kmeans is done exactly by identifying "neighbors" (at ...
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sklearn.neighbors.NearestNeighbors - knn for unsupervised learning?
From basic theory I know that knn is a supervised algorithm while for example k-means is an unsupervised algorithm.
However, at Sklearn there are is an implementation of KNN for unsupervised learning ...
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advice on distance metric for knn w/image recognition
I'm getting my feet wet with machine learning and am implementing a knn algorithm on a dataset that i've created. I've created a set of images of circles and squares and want the knn algorithm to ...
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How can I apply PCA to KNN?
I want to know the do not want to how to use library
I will denote a $n\times p$ data matrix by $X$, where $n<p$. That is, each row of $X$ is one sample data with $p$ feature variables.
By using ...
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How to calculate the silhouette coefficient?
Calculate the silhouette coefficient of point Pi from the above image.
To apply the given formula, how to know which is a(i) and b(i)?
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Knn and euclidean distance
I'm studying the knn classification algorithm.
Why can the euclidean distance be considered a nice measure of affinity between examples ?
In one dimension (1 attribute) this seems correct, but if I ...
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How to weigh feature array
I have a feature array of around 4000 elements, extracted from one source. On this array I've extracted 7 more feature from other source and now I basically have a 4007 feature array from each data ...
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How to get nearest 5 points mean with Nearest Neighbours?
In a dataset of longitude, latitude and price (of houses) I'm using sklearn's KNearestRegressor to get the 5 nearest neighbors mean price for each point. The problem is I want to do this for the whole ...
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Question about Knn and split validation
I have a big database with 40k recors and 2 classification classes. In this big database the 76% of records belong to the first class.
I've used a 70-30 split partition with stratified sampling, and ...
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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 the different staff are recorded at ...
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Evaluation metrics for Decision Tree regressor and KNN regressor
I have started working on the Decision Tree Regressor and KNN Regressor.
I have built the model and not sure what are the metrics needs to be considered for evaluation. As of now I have considered ...
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Why is cross-validation score so low?
I am using Scikit-Learn for this classification problem. The dataset has 3 features and 600 data points with labels.
First I used Nearest Neighbor classifier. ...
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Visualizing Decision Tree of K-Nearest-Neighbours classifier
I'm using Sklearn's KNN to build a classifier and was wondering if there is any way to visualize the decision tree that the algorithm builds. Maybe something of this fashion
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Is there a way to standardize latitude and longitude to be used as predictors in KNN algorithm? [closed]
The predictors are latitude and longitude, and target variable is region.
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Testing unsupervised clustering
Assume we train a KMeans model using data X. This will give a set of centroids that can be used to cluster data X* using a Nearest Centroid Classifier.
If we use a density-based model such as DBSCAN ...
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Find which type of log using machine learning?
I am the beginner of Machine learning.
The process is:
I have different log files (System log, MSSQL Server log, Linux log, MySQL Log, FTP log, IIS log).If any input is given, I will find out which ...
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How does sklearn KNeighborsClassifier compute class probabilites?
The KNeighborsClassifier has a method for predicting class probabilities. However, I cannot find any documentation describing how these probabilities are computed.
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