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Predictive Analytics brings together advanced analytics capabilities spanning ad-hoc statistical analysis, predictive modeling, data mining, text analytics, entity analytics, optimization, real-time scoring, machine learning and more. IBM SPSS Modeler puts these capabilities into the hands of business users, data scientists, and developers. In this course in the Big Data University you will learn the basics to get started with Predictive Modeling.
Module 1: Introduction to Data Mining
Question: Which of the following applications would require the use of data mining? Select all that apply.
Question: Which of the following is NOT a section of the Modeler Interface?
Question: Which of the following is NOT a part of the Cross-Industry Process for Data Mining?
Module 2: The Data Mining Process
Question: Which phase of the data mining process focuses on understanding the project requirements and objectives?
Question: Which Data Preprocessing task focuses on removing outliers and filling in missing values?
Question: The IBM SPSS Modeler supports which data type?
Module 3: Modeling Techniques
Question: Which of the following methods are commonly used for supervised learning tasks? Select all that apply.
Question: Classification is a subset of supervised learning that focuses on modeling continuous variables. True or false?
Question: Which of the following algorithms is NOT supported by the SPSS Modeler?
Module 4: Model Evaluation
Question: What is the term for a negative data point that is incorrectly classified as positive?
Question: Which of the following is NOT a cost-sensitive performance metric?
Question: What is the formula for the precision metric?
Module 5: Deployment on IBM Bluemix
Question: In general, the testing dataset should be significantly larger than the training dataset. True or false?
Question: Which of the following is NOT a model deployment solution?
Question: Which of the following statements are true of IBM Bluemix? Select all that apply.
Question: Which of the following suggests that the model is overfitting the data?
Question: Which of the following tasks would require the use of data mining?
Question: Suppose you have collected data on your customers and you wish to determine the demographics they fall into. Which technique is best suited for this task?
Question: Suppose you wish to use data mining in order to determine which customers are most likely to sign up for a new service. Which technique is best suited for this task?
Question: Which SPSS Modeler node can be used to determine a model’s performance? Select all that apply.
Question: Which of the following is NOT a classification or prediction algorithm in SPSS Modeler?
Question: Which SPSS Modeler node is used to specify whether a given field is an input or a target?
Question: Which SPSS Modeler node is useful for exploratory analysis on a data set?
Question: Which SPSS Modeler node is used to both rename fields and exclude fields from the model?
Question: What is the formula for the accuracy metric? TP = true positive, TN = true negative, FP = false positive, and FN = false negative.
Question: Which major data preprocessing step focuses on feature selection and feature extraction?
Question: Which SPSS Modeler node is used to identify missing data and screen out potentially problematic fields?
Question: SPSS Modeler provides automated tools that determine the best algorithm to use for an application. True or false?
Question: Which SPSS Modeler node is used for sampling the data set?
Question: Which phase of the data mining process focuses on gathering insights about the data set?
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