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%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u062988Chapter SixBasic Concepts of classificationClassification is a form of data analysis that extracts models describing important data classes. Such models, called classifiers, predict categorical (discrete, unordered) class labels. For example, we can build a classification model to categorize bank loan applications as either safe or risky. Such analysis can help provide us with a better understanding of the data at large. Before talking about the classification we must know the differences between supervised and unsupervised learning. Supervised learning is based on training a data sample (observations, measurements, etc.) from data source with correct classification already assigned and are accompanied by labels indicating the class of the observations , new data is classified based on the training set.In the unsupervised learning the class labels of training data is unknown and there is a given a set of measurements, observations, etc. with the aim of establishing the existence of classes or clusters in the data. Classification is considered as a supervised learning. It predicts categorical class labels (discrete or nominal) and then classifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new data. We use numeric prediction in the classification which models continuous-valued functions and predicts unknown or missing values. Applications of classification : 1- Credit/loan approval 2- Medical diagnosis as if a tumor is cancerous or benign 3- Fraud detection 4- Web page categorization Data classification is a two-step process, consisting of a learning step (where a classification model is constructed) and a classification step (where the model is used to predict class labels for given data).