K-Nearest Neighbor

This it’s an clustering approach, basically in which goal is to separated the data based on predicted similarity between the different many class. Therefore, the class are differentiate from every other by looking for similarity between given data. KNN would be a well decision when simplicity and accurate and efficiency. Are the overwhelming factor as in the “Zoo” data sit. This classification algorithms does not focus on prior probabilities, and is pretty very efficiency structurally. The primary computational are the sorting procedures in order figures out the k-nearest neighbors for the test datum. Their is many advantages. Its structural trivial, but its able to make a complex decision boundaries, it doesn’t needs much of information to be able to works, it naturaly gets in-tune with our problem-solving technique, and it quite learns easily. The disadvantage are that it take a quite long time to classify, and its pretty somewhat hard find the value best for k.

K-Nearest Neighbor

This is a clustering approach, in which the goal is to basically separate the data based on predicted similarity between the many different classes. Therefore, the classes are differentiated from each other by looking for similarities between the given data. KNN would be a good choice for when simplicity, accuracy and efficiency are required. There are overwhelming factors in the “Zoo” data set. This classification algorithm does not focus on prior probabilities and is very efficiently structured. The primary computational is the sorting procedure in order to figure out the k-nearest neighbors for the test datum. There are many advantages. Its structure is trivial, but it’s able to make complex decision boundaries, it doesn’t need much information for it to be able to work, it naturally becomes in-tune with our problem-solving techniques and it learns quickly. The disadvantages are that it takes quite a long time to classify and it’s quite hard to find the best value for “k”.

This is a clustering approach, in which the goal is to basically separate the data based on predicted similarity between the many different classes. Therefore, the classes are differentiated from each other by looking for similarities between the given data. KNN would be a good choice for when simplicity, accuracy and efficiency are required. There are overwhelming factors in the “Zoo” data set. This classification algorithm does not focus on prior probabilities and is very efficiently structured. The primary computational is the sorting procedure in order to figure out the k-nearest neighbors for the test datum. There are many advantages. Its structure is trivial, but it’s able to make complex decision boundaries, it doesn’t need much information for it to be able to work, it naturally becomes in-tune with our problem-solving techniques and it learns quickly. The disadvantages are that it takes quite a long time to classify and it’s quite hard to find the best value for “k”.

This is a clustering approach, in which the goal is to basically separate the data based on the predicted similarities between the many different classes. (Close! I was looking for this, but this one was hard to catch due to content, so it’s ok.) Therefore, the classes are differentiated from each other by looking for similarities between the given data. (Perfect!) KNN would be a good decision when simplicity, accuracy, and efficiency are the overwhelming factors, like in the “Zoo” data set. (Yeah, I threw a curveball here. As you can see, “are” should have actually continued from the previous sentence. What I like about your correction, is that it still makes grammatical and textual sense. That is really all I look for, since getting these perfect is obviously really difficult. You only missed the comma after “accuracy” with your edit.) This classification algorithm does not focus on the prior probabilities, and is very efficient in structure. (I changed this one too much, lol. Should not have done that…but you were close nonetheless. Good job!) The primary computation is the sorting procedures in order to figure out the k-nearest neighbors for the test data. (People always miss ones like that. The trick is finding the noun that that adjective is referring to. Here, there is none, so you change to “computation”. Also, with that changed, you would see that the plurality of the verb is erred; it should be “is”. Another trick is not getting confused that here, the “is” depends on the subject, not the predicate, so the plurality of the noun “procedures” has no influence over it.) There are many advantages. (Perfect!) It is structurally trivial, but it’s able to make complex decision boundaries, it doesn’t need much information to be able to work, it naturally gets in tune with our problem-solving techniques, and it learns quite easily. (Very nice! “structurally trivial” is forgivable since it was ambiguous. “gets” is synonymous with “becomes” here, so that’s ok. Should be “in tune” instead of “in-tune” and a comma after “techniques” since there are 4 total items.) The disadvantages are that it takes quite a long time to classify, and it’s somewhat hard to find the best value for k. (Almost! I was looking for “pretty hard” or “somewhat hard”. Great job overall! ^_^)