Radial Basis Functions

Radial Basis Functions (RBFs) are mathematical tools used in machine learning and interpolation to approximate complex data patterns. They function by assigning a small, localized "influence" around each data point, which decreases as you move away from that point. When combined, these influences create a smooth surface or model that fits the data well. Think of RBFs like a set of gentle, overlapping hills centered on each data point; the overall shape is built by summing these hills, making RBFs effective for functions approximation, pattern recognition, and solving various computational problems.