What is Nearest Centroid Classification? Nearest Centroid Classification Explained
Nearest Centroid Classification, also known as Centroid-based Classification, is a simple and intuitive classification algorithm that assigns new data points to the class whose centroid (mean) is closest to the data point. It is a type of prototype-based classification method.
Here are the key steps involved in this Classification:
Training phase: During the training phase, the algorithm learns the class centroids based on the labeled training data. For each class, the centroid is computed as the mean of the feature vectors of the training instances belonging to that class. Thus, the algorithm creates a centroid for each class in the feature space.
Classification phase: In the classification phase, the algorithm assigns new, unlabeled data points to one of the classes by calculating the distance between the data point and each class centroid. The data point is assigned to the class whose centroid is closest to it, based on a distance metric such as Euclidean distance or Manhattan distance.
Prediction and decision rule: Once a data point is assigned to the nearest centroid, it is classified as belonging to the corresponding class. In case of ties, where multiple centroids are equidistant from the data point, a decision rule, such as assigning the data point to the first encountered centroid or considering the class prior probabilities, may be used.
Key points about Nearest Centroid Classification:
Simple and efficient: It is computationally efficient and straightforward to implement. It does not require iterative optimization procedures like some other classification algorithms.
Assumes equal class covariances: This Classification assumes that the covariance matrices of the classes are equal. In other words, it assumes that the spread of data points within each class is similar. This assumption may not hold true for all datasets.
Sensitive to outliers: It is sensitive to outliers since it is based on the mean value of the training instances in each class. Outliers can significantly affect the centroid position and potentially misclassify data points.
Limited representation power: It may not capture complex decision boundaries as it assumes that class distributions are represented well by their centroids.
Appropriate for high-dimensional data: It can be effective for high-dimensional data since it focuses on the distances between data points and class centroids rather than considering the full feature space.
Nearest Centroid Classification is a simple and interpretable algorithm that can be useful in scenarios where data is well-separated and the assumptions of equal class covariances hold. However, it may not be suitable for complex classification problems where more sophisticated algorithms might be required.
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