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These questions relate to R programming language Data Mining and Business Analytics with R The following six questions are short essay questions.
These questions relate to R programming language
Data Mining and Business Analytics with R
The following six questions are short essay questions. To answer each question, 5 to 10 sentences should suffice.
1) The following diagram (in attachment) depicts three categories of analytical methods and models. For each category of analytics, briefly describe its main focus, objectives and representative analytic tools. Why are most data mining tools primarily used for predictive analytics? [18 points]2) What’s the difference between data frames and data matrices in R? Briefly describe how to convert a data matrix to a data frame in R? [16 points]
3) Briefly explain why the predicted values generated by binary logistic regression models are always between 0 and 1? Why can we interpret the coefficients of a logistic regression model using odds ratios? [16 points]
4) Briefly explain why it is usually not objective to evaluate a classifier’s performance based on the training data set? Why poor out-of-sample predictive accuracy sometimes signals the problem of overfitting? [16 points]
5) Briefly explain two ways to limit overfitting in constructing a decision tree. Briefly explain the advantages and the weaknesses of decision trees. [16 points]
6) List four common properties of distance measures such as the Euclidean distance. Why it is often necessary to standardize your data when calculating the Euclidean distance? Briefly explain the four distance measures we often use to calculate the distance between two clusters. [18 points]
