Kernels

In machine learning, kernels are mathematical functions that enable algorithms to analyze and interpret data in complex, higher-dimensional spaces without explicitly transforming the data. Think of them as tools that measure the similarity between data points, allowing models like Support Vector Machines to identify patterns or boundaries efficiently. This approach helps in solving tasks like classification or regression when data isn't linearly separable in its original form. Essentially, kernels provide a powerful way to handle complex relationships in data by implicitly mapping it into spaces where analysis becomes more straightforward.