63) List and briefly describe the six steps of the Crisp-dm data mining process Ar nswer Step 1: Business Understanding- The key element of any data mining study is to know what the study is for. Answering such a question begins with a thorough understanding of the managerial need for new knowledge and an explicit specification of the business objective regarding the study to be conducted Step 2: Data Understanding- A data mining study is specific to addressing a well-defined business task, and different business tasks require different sets of data. Following the business understanding, the main activity of the data mining process is to identify the relevant data from many available databases Step 3: Data Preparation-The purpose of data preparation(or more commonly called data preprocessing)is to take the data identified in the previous step and prepare it for analysis by data mining methods. Compared to the other steps in CRISP-DM, data preprocessing consumes the most time and effort; most believe that this step accounts for roughly 80 percent of the total time spent on a data mining project Step 4: Model Building- Here, various modeling techniques are selected and applied to an already prepared data set in order to address the specific business need. The model-building step also encompasses the assessment and comparative analysis of the various models built Step 5: Testing and Evaluation--In step 5, the developed models are assessed and evaluated for their accuracy and generality. This step assesses the degree to which the selected model(or models)meets the business objectives and if so to what extent(i.e. do more models need to be Step 6: Deployment- Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise. In many cases, it is the customer, not the data analyst, who carries out the deployment Diff: 2 Page Ref: 207-212 64) Describe the role of the simple split in estimating the accuracy of classification models Answer: The simple split(or holdout or test sample estimation) partitions the data into two mutually exclusive subsets called a training set and a test set(or holdout set). It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set. The training set is used by the inducer(model builder), and the built classifier is then tested on the test set. An exception to this rule occurs when the classifier is an artificial neural network. In this case, the data is partitioned into three mutually exclusive subsets: training, validation, and testing Diff: 2 Page Ref: 217 Copyright C 2018 Pearson Education, Inc10 Copyright © 2018 Pearson Education, Inc. 63) List and briefly describe the six steps of the CRISP-DM data mining process. Answer: Step 1: Business Understanding — The key element of any data mining study is to know what the study is for. Answering such a question begins with a thorough understanding of the managerial need for new knowledge and an explicit specification of the business objective regarding the study to be conducted. Step 2: Data Understanding — A data mining study is specific to addressing a well-defined business task, and different business tasks require different sets of data. Following the business understanding, the main activity of the data mining process is to identify the relevant data from many available databases. Step 3: Data Preparation — The purpose of data preparation (or more commonly called data preprocessing) is to take the data identified in the previous step and prepare it for analysis by data mining methods. Compared to the other steps in CRISP-DM, data preprocessing consumes the most time and effort; most believe that this step accounts for roughly 80 percent of the total time spent on a data mining project Step 4: Model Building — Here, various modeling techniques are selected and applied to an already prepared data set in order to address the specific business need. The model-building step also encompasses the assessment and comparative analysis of the various models built. Step 5: Testing and Evaluation — In step 5, the developed models are assessed and evaluated for their accuracy and generality. This step assesses the degree to which the selected model (or models) meets the business objectives and, if so, to what extent (i.e., do more models need to be developed and assessed). Step 6: Deployment — Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise. In many cases, it is the customer, not the data analyst, who carries out the deployment steps. Diff: 2 Page Ref: 207-212 64) Describe the role of the simple split in estimating the accuracy of classification models. Answer: The simple split (or holdout or test sample estimation) partitions the data into two mutually exclusive subsets called a training set and a test set (or holdout set). It is common to designate two-thirds of the data as the training set and the remaining one-third as the test set. The training set is used by the inducer (model builder), and the built classifier is then tested on the test set. An exception to this rule occurs when the classifier is an artificial neural network. In this case, the data is partitioned into three mutually exclusive subsets: training, validation, and testing. Diff: 2 Page Ref: 217