A polynomial kernel is a type of kernel function used in machine learning, particularly in support vector machines (SVMs). It allows algorithms to operate in a higher-dimensional space without explicitly transforming the data. This is achieved by computing the inner product of the input data raised to a specified power, which can capture non-linear relationships. The polynomial kernel is defined as K(x, y) = (x \cdot y + c)^d , where c is a constant, and d is the degree of the polynomial. By adjusting these parameters, users can control the complexity of the decision boundary, making it flexible for various datasets.