Which of the following kernels is most likely to suffer from overfitting?

The Gaussian kernel, also known as the radial basis function (RBF) kernel, has a tendency to suffer from overfitting more readily compared to other kernels because of its inherent flexibility. The Gaussian kernel can create very complex decision boundaries in high-dimensional spaces due to its ability to evaluate the spatial relationships of data points very closely. This means that it can fit the training data with high accuracy, capturing even the smallest variations or noise in the dataset, which can lead to poor generalization on unseen data.

In contexts where the training set is small or contains noise, the Gaussian kernel can easily create a model that is overly complex, leading to overfitting. It adjusts too closely to the data points, including outliers, rather than finding a simpler pattern that may be more representative of the underlying trend of the data.

Meanwhile, linear kernels create a more explicit linear separation between classes and are less prone to overfitting because they lack the flexibility to capture complex relationships in the data. The polynomial kernel can also lead to overfitting, especially with higher degrees, but not to the same extent as the Gaussian kernel, which can adjust more finely to the distribution of the data. The sigmoid kernel, while more similar to neural networks and capable of creating