Given a dataset where the dataset is not linearly-separable, and each of the fea- tures have continuous values, which of the following algorithms is more ideally suited a) perceptron b) decision-trees c) neural-networks. Why

Option c neural-networks

Explanation:

Since the dataset is not linearly-separable, the perceptron is not a good option.  A single layer perceptron can only work on linearly separable dataset. On another hand, since the features have continuous values, the decision-trees is not a good option. The continuous values will result in a extremely complicated tree structures which may expect a very high computational cost for a simple prediction. The most ideal choice is neural-networks at it can learn the complicated non-linear pattern and produce the optimum predictive model.

8 track im pretty sure came first