Which is more likely to result in overfitting a model? More training data, decreased nodes in neural network hidden layers, eliminating sparse features, or using a Gaussian or RBF kernel in SVM?

Using a Gaussian or RBF kernel in an SVM is more likely to result in overfitting compared to the other options. These kernels introduce non-linearity and increase model complexity, which can lead to fitting the training data too closely, including noise, especially if the underlying data relationship is simpler. Conversely, more training data, fewer nodes in hidden layers, and eliminating sparse features generally help to reduce complexity and combat overfitting.