This inquiry pertains to the issue of overfitting in machine learning. Overfitting happens when a model is tailored so precisely to the training data that it struggles with new, unseen data. The candidate is required to determine which of the provided actions is most likely to lead to an overfitted model. The options include enlarging the training dataset, decreasing the number of nodes in a neural network's hidden layers, eliminating sparse or infrequent features, or altering the kernel function in a Support Vector Machine (SVM) from linear to Gaussian or RBF (Radial Basis Function). To effectively respond to this question, it's essential to grasp the concept of overfitting and the impact of each action on the likelihood of the model overfitting. Here are the main points to consider: In general, increasing the amount of training data is advantageous for the model and can aid in preventing overfitting by providing more examples for the model to learn from. Reducing the number of nodes in a neural network's hidden layers may also lessen overfitting, as it diminishes the model's complexity. Removing sparse or less frequent features can help decrease noise and mitigate overfitting. Changing the kernel in SVMs from linear to Gaussian or RBF raises the model's complexity. While more complex models can closely fit the training data, they are also more prone to overfitting.

Among the provided options, changing from a linear kernel to a Gaussian or RBF kernel in a Support Vector Machine (SVM) is the most probable cause of overfitting. SVMs that utilize Gaussian or RBF kernels exhibit greater model complexity than those using linear kernels, making them more prone to closely fitting the training data while generalizing poorly to new data. In terms of increasing the amount of training data, this typically aids in minimizing overfitting. Additionally, reducing the number of nodes and removing sparse features can also decrease the model's complexity, thereby lowering the chances of overfitting.

Among the options given, the action most likely to cause overfitting in a model is changing from a linear kernel to a Gaussian or RBF kernel in an SVM. This is due to the fact that Gaussian and RBF kernels create more adaptable decision boundaries, which can result in the model fitting too closely to the training data, thereby leading to overfitting. Conversely, in general, increasing the amount of training data, reducing the number of nodes in hidden layers, and removing less common features typically lower the risk of a model overfitting by either simplifying the model or providing it with greater variability to learn from.