1. What is a classification model? As the classification model serves two vital
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Question
1. What is a classification model? As the classification model serves two vital functions in data mining: predictive and descriptive model. Explain the models’ critical application with relation to an industry of your choice?
2. Describe a decision tree? From chapter 3, Figure 3.4 depicts a decision tree for the mammal classification problem. The tree has three types of nodes: a root node, internal node, and leaf nodes. Each node is associated with a class label. Describe a similar decision tree with four nodes and explain the class labels?
Explanation / Answer
Answer: 1)
Classification Model: Classification is the category of supervised learning i.e., where the targets/class labels are known with the input data. Classification model helps in prediction based existing data. Classification is used to classify each item in a set of data into one of a predefined set of classes or groups. It includes many applications such as in credit approval, customer segmentation, business modeling, medical diagnosis, target marketing and email classification etc.
There are two primary data mining models: 1) Predictive and 2) Descriptive.
Predictive Modeling is a tool that uses statistical techniques, machine learning, and data
mining to discover facts in order to make predictions about unknown future events, i.e., it addresses the questions like what will happen? and why will it happen?. For example, Classification, Regression, Time series analysis, Prediction are the the data mining applications,mean to forecast the future state of the data(i.e., it is the process of investigation on existing and previous data behaviour, and predicting the future state of the the data).
Descriptive Modeling the names itself implies that it will "Describe”, or summarize raw data and tries know something about the data that is interpretable by humans(i.e., it describes about the past data and answer the questions like “What has happened?). These models will help us to learn something about past data behaviour and provides information about the data about how it could influence in future. It' application includes custer segmentaion, value based segmentaion, behaviour based segmentaion and needs based segmentaion etc,.
Descriptive modeling helps tothe organization to understand their customers, but predictive modeling facilitates the desired outcomes. Both descriptive and predictive modeling are the key elements of data mining.
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