Classification techniques used in Mining student performance in classroom
It could be some time but not necessarily advisable predictive modeling is seen as a “black box” that makes predictions about the future based on information from the past, and present. Some designs are better than others in terms of accuracy. Some designs are better than others in terms of understanding. For example, models from better understanding of the incomprehensible decision trees, rule induction, and regression models, neural networks. The classification is a type of predictive models. More specifically, the ranking is the appointment process of new objects or predefined categories: given a set of marked files, build a model such as the decision tree, and predicting future records labels is called for classes such of them:
· Decision Tree :
Decision trees are trees that classify instances by sorting them
based on feature values. Each node in a decision tree represents a feature in an instance
to be classified, and
each branch represents a value that the node can assume. Instances are classified starting at the root node and sorted based on their feature values. An example of a decision tree for the training set. Decision tree is a predictive model which, as its name suggests, can be seen as a tree. Specifically each branch of the tree is a classification of matter and leaves of trees and data sections with classified, and this technique which is applied in this research.
· Bayesian Networks :
A Bayesian Network (BN) is a graphical model for probability relationships among a set of variables features. The Bayesian network structure S is a directed acyclic graph DAG) and the nodes in S are in one-to-one correspondence with the features X. The arcs represent casual influences among the features while the lack of possible arcs in S encodes conditional independencies Moreover, a feature (node) is conditionally independent from its non- descendants given its parents (X1 is conditionally independent from X2 given X3 if P (X1|X2, X3) =P (X1|X3) for all possible values of (X1, X2, X3).
· K-nearest neighbor classifiers :
Nearest neighbor classifiers are based on learning by analogy. The training samples are described by n dimensional numeric attributes. Each sample represents a point in an n- dimensional space. In this way, all of the training samples are stored in an n-dimensional pattern space. When given an unknown sample, a k-nearest neighbor classifier searches the pattern space for the k training samples that are closest to the unknown sample. “Closeness” is defined in terms of Euclidean distance, where the Euclidean distance, where the Euclidean distance between two points.
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