I’m working on a computer science discussion question and need an explanation to help me study.
What are the various types of classifiers?
Rule-based systems,nearest neighbor classifiers,Bayesian classifiers, artificial neural networks, including deep learning, support vector machines, and ensemble classifiers, which are collections of classifiers.
What is a rule-based classifier?
To categorize data instances, a rule-based classifier employs a set of “if…then…” rules . Rule-based classifiers, like decision trees, can handle a variety of categorical and continuous attributes and operate well in multiclass classification settings. Rule-based classifiers are commonly used to create descriptive models that are easier to read than decision tree classifiers yet perform similarly. Because a decision tree can be represented by a set of mutually exclusive and exhaustive rules, the expressiveness of a rule set is nearly similar to that of a decision tree. Because irrelevant characteristics provide low information gain, rule-based classifiers can avoid picking them if other relevant attributes provide higher information gain. Missing values in the test set are problematic for rule-based classifiers. This is due to the fact that rules in a rule set are ordered in a specific way, and even if a test instance is covered by many rules, they can give various class labels depending on where they are in the rule set.
What is the difference between nearest neighbor and naïve bayes classifiers?
The main distinction between the K-NN and the Naive Bayes classifiers is that the former is a discriminative classifier, whilst the latter is a generative classifier. The K-NN classifier is a local heuristic supervised lazy classifier. It’s tough to utilize this for real-time prediction because it’s a lazy classifier. K-NN achieves significantly more complex decision boundaries than any decision tree, resulting in a great classification. Because of its natural ability to optimize locally, K-NN performs better when tackling a problem that directly focuses on discovering similarity between data. Naive Bayes is a quick eager learning classifier that outperforms K-NN.
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