The sklearn.neighbors.KernelDensity module in Python's scikit-learn library is a class that allows for the estimation of probability density functions using kernel functions. The KernelDensity class uses a non-parametric approach, which means it does not make any assumptions about the underlying distribution of the data. This makes it useful in a variety of applications, such as density estimation and anomaly detection. The KernelDensity class provides methods to fit the model to the data and generate probability density estimates at specific points. It also offers flexibility in choosing different kernel functions and bandwidth parameters to tune the accuracy and performance of the density estimation.
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