In pattern recognition, k-nearest neighbors (k-NN) is a classification algorithm used to classify example based on a set of already classified examples.
I'm currently attempting to find K Nearest Neighbor of all nodes of a balanced KD-Tree (with K=2). My implementation is …
algorithm nearest-neighbor kdtree knnI'm looking to perform classification on data with mostly categorical features. For that purpose, Euclidean distance (or any other numerical …
r distance knnI have a large set of features that looks like this: id1 28273 20866 29961 27190 31790 19714 8643 14482 5384 .... upto 1000 id2 12343 45634 29961 27130 33790 14714 7633 15483 4484 .... id3 ..... ..... ..... ..... ..... ..... .... ..... .... .... . . . ... id200000 .... .... ... .. . . . . I want to compute …
python knnAlmost all of the examples are based on numbers. In text documents i have words instead of numbers. So can …
svm knn document-classification text-classificationI'm using kaggle's pokemon data to practice KNN imputation via preProcess(), but when I did I encountered this following message …
r na knnI am currently trying to implement an ROC Curve for my kNN classification algorithm. I am aware that an ROC …
python machine-learning scikit-learn knnI'm trying to perform my first KNN Classifier using SciKit-Learn. I've been following the User Guide and other online examples …
scikit-learn cross-validation knn grid-searchI am trying to implement kNN from the fancyimpute module on a dataset. I was able to implement the code …
python machine-learning knn imputationSo I want to be able to recognise patterns in images (such as a number 4), I have been reading about …
ocr image-recognition knn