The correct answer is D. perceptron.
A perceptron is a type of artificial neural network that can be used for classification and regression tasks. It is a linear model, which means that it can only learn a linear relationship between the input features and the output labels. This means that a perceptron can only achieve zero training error on a linearly separable dataset, which is a dataset where the data points can be separated into two classes by a linear decision boundary.
A decision tree is a type of supervised learning algorithm that can be used for classification and regression tasks. It is a non-parametric model, which means that it does not make any assumptions about the underlying distribution of the data. This makes decision trees more robust to noise and outliers than linear models like perceptrons.
15-nearest neighbors is a type of lazy learning algorithm that can be used for classification and regression tasks. It is a non-parametric model, which means that it does not make any assumptions about the underlying distribution of the data. This makes 15-nearest neighbors more robust to noise and outliers than linear models like perceptrons.
A hard-margin SVM is a type of supervised learning algorithm that can be used for classification and regression tasks. It is a non-parametric model, which means that it does not make any assumptions about the underlying distribution of the data. This makes hard-margin SVMs more robust to noise and outliers than linear models like perceptrons.
In conclusion, the correct answer is D. perceptron.