Huawei Certified ICT Associate – Artificial Intelligence (HCIA-AI) Practice Exam

The use of the 'rbf' kernel in the provided Support Vector Regression (SVR) code indicates that a Radial Basis Function kernel is being employed rather than a linear kernel function. The 'rbf' kernel is nonlinear and is commonly used for problems where the relationship between the features and the target variable is not linearly separable.

Setting the kernel to 'linear' would involve a different specification within the code. In contrast, the 'rbf' kernel function allows the model to handle more complex patterns in the data. The parameters 'C' and 'gamma' in the SVR function play significant roles in controlling the regularization and the influence of each training example, but they do not change the fact that a linear kernel is not being used in this scenario.

Therefore, stating that the code does mean we use linear kernel functions is incorrect; the presence of 'rbf' clearly designates it as a nonlinear kernel. This reinforces the choice that the assertion is false.