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What is the difference between these algorithms? Decision Tree - Naive Bayes - Apriori Algorithm - Multi Regression Model

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closed as not constructive by Anony-Mousse, Aziz Shaikh, Matti Lyra, Edwin de Koning, UmNyobe Nov 20 '12 at 11:25


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Some algorithm like naive bayes and Decision tree works on labeled data where you have a classification column. For example if you want to relate the weather status and day of week with the punctuality of train then it should be labeled data. Because you have lot of combination of weather and day of week and you have a class column containing values late/not late.

On the other hand Apriori Algorithm works on unlabeled data where you don't have class column. For example if a customer buy A and B then most probably he will buy C. Here there is no class column. Any item can come in the decision. This algorithm is used to find association rule and mainly find the frequent item sets from the data sets,

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Sorry, but this is a very bad question. So all you can expect to get is a bad answer, sorry.

You are throwing in some random algorithms, and ask us to explain the difference.

But they are so different, it is hard to find a place to start.

APRIORI and Decision Trees solve completely different problems. So they are about as similar as Apples and Bananas. Both happen to be fruit, but they are, well, different.

Please do some more research, read at least what Wikipedia has on the topic (or a book. You know, there are some pretty good machine learning books) and look at the FAQ.

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