Distance-based Methods
Distance-based methods are a class of machine learning algorithms that rely on measuring the distance between data points to make predictions or classify new data. These methods are commonly used in clustering, anomaly detection, and classification tasks. The distance metric used can vary depending on the problem and the type of data being analyzed. Common distance metrics include Euclidean distance, Manhattan distance, and cosine similarity. Distance-based methods are particularly useful when dealing with high-dimensional data, as they can effectively capture the relationships between data points in a multi-dimensional space. However, they can be sensitive to outliers and noisy data, which can affect the accuracy of the results.
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