kerneltihedusfunktsioon
Kerneltihedusfunktsioon, often abbreviated as KDF, is a non-parametric statistical method used to estimate the probability density function of a random variable. In simpler terms, it's a way to smooth out a histogram to create a more continuous and accurate representation of the underlying distribution of data. Instead of just counting occurrences within bins like a histogram, the KDF places a "kernel" function, typically a smooth curve, at each data point and sums these kernels to create a smooth estimate of the density.
The choice of kernel function is generally less critical than the choice of bandwidth. Common kernel functions
Kerneltihedusfunktsioon has widespread applications in various fields, including machine learning for density estimation, anomaly detection, and