Depp Dive | C++ Machine Learning Tutorial Part 2: K-Nearest Neighbors (KNN)
Автор: Clean CodeX
Загружено: 2024-11-28
Просмотров: 146
The creation of a K-Nearest Neighbors (KNN) algorithm in C++. The tutorial focuses on building the algorithm from the ground up, starting with file structure (include, source, and makefiles) and class definition. The core of the algorithm involves calculating Euclidean distances between a new data point and existing training data to find the K nearest neighbours, subsequently predicting the class of the new data point based on the most frequent class among these neighbours. The code includes functions for calculating distance, finding the nearest neighbours, and determining the predicted class, alongside performance validation using test and validation datasets. Finally, the tutorial demonstrates how to determine the optimal value of K, a crucial parameter in the algorithm, by iteratively testing and comparing performance metrics.
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