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How does classification work?
Classification is a data-mining approaches that assigns elements to a set of data to aid in more efficient predictions and analysis. The classification is generally used when there are two target classes known as binary classification.
When higher than two classes can be predicted, especially in pattern recognition problems, this is defined as multinomial classification. However, multinomial classification can be used for categorical response data, where one needs to predict which category amongst various elements has the instances with the largest probability.
Data classification is a two-step phase. In the first phase, a classifier is built defining a predetermined collection of data classes or concepts. This is the learning phase (or training phase), where a classification algorithm develops the classifier by analyzing or “understanding from” a training set create up of database tuples and their related class labels.
A tuple, X, is described by an n-dimensional attribute vector, X = (x1, x2, … xn), defining n measurements create on the tuple from n database attributes, accordingly, A1,A2,... An.
Every tuple, X, is considered to belong to a predefined class as decided by another database attribute known as the class label attribute. The class label attribute is discrete-valued and unordered. It is categorical in that every value provide as a category or class.
The single tuples creating up the training set are defined as training tuples and are choose from the database under analysis. In the framework of classification, data tuples can be defined as samples, instances, data points, or objects.
Because the class label of every training tuple is supported, this step is called a supervised learning. It can compare with unsupervised learning (or clustering), in which the class label of every training tuple is not popular, and the number or set of classes to be understand cannot be known in advance.
In the second phase, the model can be used for classification. First, the predictive accuracy of the classifier is predicted. If it can use the training set to calculate the accuracy of the classifier, this estimate can be optimistic, because the classifier tends to overfit the records (i.e., during learning it can incorporate some specific anomalies of the training records that are not present in the general data set complete).
Hence, a test set is utilized, create up of test tuples and their related class labels. These tuples are randomly choosed from the general data set. They are separate of the training tuples, defining that they are not used to make the classifier.
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