Classification

classification section is the algorithm to solve classification problem

Basic Concept of Classification

Classification: It is a data analysis task, i.e. the process of finding a model that describes and distinguishes data classes and concepts. Classification is the problem of identifying to which of a set of categories (subpopulations), a new observation belongs to, on the basis of a training set of data containing observations and whose categories membership is known. Example: Before starting any project, we need to check its feasibility. In this case, a classifier is required to predict class labels such as ‘Safe’ and ‘Risky’ for adopting the Project and to further approve it. It is a two-step process such as :

Learning Step (Training Phase): Construction of Classification Model Different Algorithms are used to build a classifier by making the model learn using the training set available. The model has to be trained for the prediction of accurate results.
Classification Step: Model used to predict class labels and testing the constructed model on test data and hence estimate the accuracy of the classification rules.

Training and Testing: Suppose there is a person who is sitting under a fan and the fan starts falling on him, he should get aside in order not to get hurt. So, this is his training part to move away. While Testing if the person sees any heavy object coming towards him or falling on him and moves aside then the system is tested positively and if the person does not move aside then the system is negatively tested. Same is the case with the data, it should be trained in order to get the accurate and best results.
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