Fill in the table by answering with yes/no whether decision trees, k-nearest neighbor (kNN), linear hard-margin support vector machine (SVM), and neural networks (NN) can easily handle the following eight properties of a given classification problem:

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Problem properties Binary features Numeric features Categorical features Robust to noisy training examples Fast classification is crucial Many irrelevant features Relevant features have different scale Data is not linearly separable Decision trees kNN Linear SVM Yes NN Problem properties Binary features Numeric features Categorical features Robust to noisy training examples Fast classification is crucial Many irrelevant features Relevant features have different scale Data is not linearly separable Decision trees kNN Linear SVM Yes NN