Please provide your in-depth response to the below discussion. If you agree/disagree and why?

The k-nearest neighbors' algorithm The k-nearest neighbors' algorithm is also known as the KNN algorithm andis a supervised machine learning technique that can be used to address both linear and non - linear and is uncomplicated to deploy.KNN algorithm is based on the observation anticipated on its resemblance to previous observations, in other ways, a simplistic technique to classify data. A closed-form framework can't assess KNN because it's a heap method. This indicates that the training samples are required at run-time, and predictions are derived from statistical associations.

To explain this algorithm we can take color as different categories, if we have a set with post classifications (like green red, blue, etc.), we may aggregate the facts together and detect a new alien category. For this newfound foreign element, we initially don't know what category it belongs to but we can do so by looking at the labeled categories that are closest to it, or its neighbors. We also look at the value of K to see how many neighbors we need to evaluate in relation to the distance. If K is 10, we look at which ten categories are near to it; for example, if we have three green and two red categories that are closest to the extraterrestrial, we will choose the green category with the most votes.

2.Bayes' algorithm for estimating the probability

Bayes' Algorithm is the formula that is used to calculate the likelihood of an occurrence based on prior information that may be considered important. Classifiers, on the other hand, are a set of Bayes Theorem-based classification methods.

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