Final answer:
In Support Vector Machines (SVM), a larger C value makes the model less tolerant to classification errors and margin violations, potentially leading to overfitting the training data.
Step-by-step explanation:
In the context of Support Vector Machines (SVM), the parameter C plays a crucial role in determining the trade-off between the simplicity of the decision surface and the classification errors on the training data. The larger the value of C, the less tolerant the SVM is to classification errors or, in other words, to the margin violations on the training data. A high value of C aims at minimizing the number of misclassifications, but it might lead to a more complex decision boundary that potentially overfits the training data. In contrast, a smaller value of C allows more misclassifications but seeks a simpler decision boundary that might generalize better to unseen data.