<ul><li>Kernel Density Estimation (KDE) is a non-parametric technique that estimates the underlying distribution by smoothing data points with localized kernel functions.</li><li>KDE produces a smooth, continuous estimate of the distribution and does not require manually choosing bin sizes like histograms.</li><li>KDE has applications in anomaly detection, density-based clustering, feature engineering, and generative modeling in machine learning.</li><li>Challenges with KDE include computational complexity and bandwidth selection, which can be optimized using techniques like Fast Fourier Transform or cross-validation.</li></ul>

Kernel Density Estimation (KDE) – A Non-Parametric Approach to Probability Estimation

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