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

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Computers & Technology

Speedo

24 April, 05:04

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

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Answers (1)

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Briley Cardenas · Answer 1

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.