The decision tree is a classification model, applied to existing data. If you apply it to new data, for which the class is unknown, you also get a prediction of the class. The assumption is that the new data comes from the similar distribution as the data you used to build your decision tree. In many cases this is a correct assumption and that is why you can use the decision tree for building a predictive model.
There is only a slight difference between discrimination and classification in data mining. Discrimination can be negative and classification is generally just factual.
defect prevention includes those strategies through which we avoid to occur a defect. for example formal risk analysis, prototyping. in defect prediction those areas are highlighted where there is possibility that defect can occur.
I've been looking for this aswer about a few months, and nothing! Researching on it, I believe that both are same. But, with only one markable difference: clustering is a type of unsupervised learning, and classification is a type of supervised learning. I believe that it is the only difference, and, of course, this dictates the way that the algorithm starts. But the results are essentially similar: grouped data.Good luck in your question. I hope I've helped!
what is the difference between license and patent
what is the difference between an assembler and the translator
difference between knowledge classification and book classification?
prediction is when you do not know what will happen but you guess that it will happen. observation is when you see something
There is only a slight difference between discrimination and classification in data mining. Discrimination can be negative and classification is generally just factual.
The only difference is between a prediction made by a man and a prediction of God
Really, there is no difference except for the name, and the fact that a hypothesis is a bit more formal.
Rock my name is ebony it has a lot of thing that you can make
A prediction is what you think will happen BEFORE the experiment is followed through with, a conclusion is what you observe and conclude after the experiment has been completed.
Observation is the act of noticing and recording something that has been directly perceived. Inference involves making logical conclusions based on observations and prior knowledge. Prediction is a statement about what will happen in the future based on observations, inferences, and patterns.
data classification in statistics
Modern classification is based on evolutionary relationships between organisms while traditional classification is not.
Classification is sorting out things due to scientific process. Partition is eminent domain.
so scientists can tell the difference between animals