Kernel Density Estimation (KDE)
A non-parametric way to estimate the probability density function of a random variable, often used in exploratory data analysis to visualize the distribution of data.
Implications
A non-parametric method for estimating the probability density function of a random variable, often used in statistics and data analysis to identify patterns, distributions, and trends in data, providing a smooth estimate of the underlying distribution without assuming a specific parametric form.
Example
Example: A data scientist uses Kernel Density Estimation to analyze the distribution of customer purchase amounts, revealing peaks and trends that help in understanding consumer behavior and optimizing pricing strategies.
Related Terms
Different from histograms, which provide a binned and often less smooth representation of data, KDE provides a continuous estimate of the data distribution, offering more nuanced insights into the underlying patterns.
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