Bandwidth Parameter
In data science, the bandwidth parameter is a key parameter in kernel density estimation (KDE) which determines the smoothness of the estimated probability density function. It controls the trade-off between bias and variance in the estimation process. A small bandwidth results in a highly variable and jagged density estimate, while a large bandwidth results in a smoother but more biased estimate. The optimal bandwidth can be selected using cross-validation techniques. In machine learning, the bandwidth parameter is also used in clustering algorithms such as Mean Shift and DBSCAN to determine the size of the neighborhood around each data point. A larger bandwidth results in larger clusters and vice versa.
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