Kernel Trick

The Kernel Trick is a method used in machine learning to handle complex data patterns without explicitly transforming the data into high-dimensional space. Instead, it uses a mathematical function called a kernel to compute similarities between data points directly, making it easier for algorithms like Support Vector Machines to find decision boundaries. Think of it as a shortcut that allows the model to work with complex, nonlinear data efficiently, without the computational burden of mapping all data points into a much larger, more complicated space.