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Most often, it is used for classification, as a k-NN classifier, the output of which is a class membership. An object is classified by a plurality vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors ( k is a positive integer , typically small).
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Structured k-nearest neighbours (SkNN) [1] [2] [3] is a machine learning algorithm that generalizes k-nearest neighbors (k-NN). k-NN supports binary classification, multiclass classification, and regression, [4] whereas SkNN allows training of a classifier for general structured output.
Empirically, for machine learning heuristics, choices of a function that do not satisfy Mercer's condition may still perform reasonably if at least approximates the intuitive idea of similarity. [6] Regardless of whether k {\displaystyle k} is a Mercer kernel, k {\displaystyle k} may still be referred to as a "kernel".
Classifier chains is a machine learning method for problem transformation in multi-label classification. It combines the computational efficiency of the binary relevance method while still being able to take the label dependencies into account for classification .
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. [4] [5] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and ...
Finally classifier is generated by using the previously created set of classifiers on the original dataset , the classification predicted most often by the sub-classifiers is the final classification for i = 1 to m { D' = bootstrap sample from D (sample with replacement) Ci = I(D') } C*(x) = argmax #{i:Ci(x)=y} (most often predicted label y) y∈Y
Large margin nearest neighbor (LMNN) [1] classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming , a sub-class of convex optimization .