Naive Bayes
The Naive Bayes classification is a well medium to many user modeling situations as in the “Iris” data sit given it’s advantages of fast learning, nor intuition, and structural low costs. It work would the following way: Suppose you’re data consist of vegetable described by their color and shape. This would work by say "If you saw a vegetable that is green and spherical, what type of vegetable it is most likely to be, base on data? In future, classify yellow and spherical vegetable as that type of vegetables." The advantages of this are that it work good on text and numerical data, and is easy to implement and compute when comparing to other classification algorithm. The disadvantages are that it do not do good at all when feature are highly dependant. Also, and it do not considers multiples or repeat of same data.

The Naive Bayes classification is a well medium to many users modeling situations as in the “Iris” data, it gives it’s advantages of fast learning, intuition, and structural low costs. It would work the following way: Suppose you’re data consist of vegetables described by their color and shape. This would work by saying "If you saw a vegetable that is green and spherical, what type of vegetable is it most likely to be, based on data? In the future, classify yellow and spherical vegetables as that type of vegetable." The advantages of this are that it would work good in text and numerical data, and is easy to implement and compute when comparing to other classification algorithms. The disadvantages are that it does not do good at all when features are highly dependent. Also, it does not consider multiple or repeating of the same data.

The Naive Bayes classification is a well (good is the word that goes here) medium to many users modeling (it’s user modeling, or user-modeling) situations (need a comma here) as in the “Iris” data, it gives it’s advantages of fast learning, intuition, and structural low costs. (Not what I was looking for here, but I see why you changed it to that. Even so, though, there were several mistakes in the case that that was preferred, and they were “data set. It” and “its advantages”) It would work the following way: Suppose you’re data consist of vegetables described by their color and shape. (The problem here is tense parallelism. If “described” is past, then “consist” should be past as well, therefore, “consisted”.) This would work by saying "If you saw a vegetable that is green and spherical, what type of vegetable is it most likely to be, based on data? (Same problem as before with the verb tenses. If you use “is” then you can’t use “saw”, so it’s “If you see a vegetable…”. Also, the end has to be “based on the data?”) In the future, classify yellow and spherical vegetables as that type of vegetable." (I threw this one in there for extra credit, so it’s ok. This was an “understanding text” problem. Should be “…classify green and spherical vegetables as that type of vegetable.”) The advantages of this are that it would work good in text and numerical data, and is easy to implement and compute when comparing to other classification algorithms. (Good job on the second part of this sentence, but the first part is erred. Should be “…are that it works well on text…”) The disadvantages are that it does not do good at all when features are highly dependent. (Nice catch on “dependent” instead of “dependant”, but before, “good” should be “well”) Also, it does not consider multiple or repeating of the same data. (Almost… ”Also, it does not consider multiples or repeats of the same data.”)