From these three classification approaches (decision trees, Naïve Bayes and neural networks), both with their own advantages and shortcomings. Give a real-world business problem that can be solved via classification and discuss which classification approach may be more suitable for this problem. In your discussion, consider the trade-offs regarding predictive performance, computational requirements, data size, and the interpretability of the prediction rules.
Out of the three mentioned classification approaches (decision trees, Naive Bayes and neural networks) the NB classifier not only outperforms the other classifier but also achieved an impressive results in the training courses domain.
NB models are popular in machine learning applications because of their simplicity in allowing each attribute to contribute towards the final decision equally and independently from the other attributes. This simplicity equates to computational efficiency, which makes NB techniques attractive and suitable for many domains.
Unlike NB classifiers, DT classifiers can handle combinations of terms and can give excellent results for few domains. However, training a DT classifier is quite difficult and they can get out of hand with the number of nodes created in some cases.
NNs are strong techniques for showing complex relationships between input and outputs. Considering the neural structure of the brain. NNs are difficult and they can be enormous for some domain, containg large number of nodes and synapses.
In experiments, NB classifier can easily classify training web pages with 95.20% accuracy and an F-Measure value of over 97%. The NB approach can be chosen as thorough analysis of many web pages. This approach is also a practical choice, because ATM, like many small companies, has limited hardware specifications available at their premises, which needed to be taken into account.
From these three classification approaches (decision trees, Naïve Bayes and neural networks), both with their own...