ISYE 6501 COMPLETE SOLUTION PACK|GRADED A+

What is modeling? Describing a real life situation mathematically Model Mathematical approaches to solving analytics problems 1. Regression for ex. can be called the model of choice. 2. If more detail (descriptive and numerical), the use of, for ex. Regression, can be called the model. 3...

Descriptive Analytics What happened Predictive Analytics What will happen 00:03 01:24 Prescriptive Analytics What action(s) would be best algorithm Step-by-step procedure designed to carry out a task. change detection Identifying when a significant change h...

What does SVM stand for? Support Vector Machine Is written text structured or unstructured? Unstructured 00:45 01:24 When we increase the sum of the square of the coefficients we... Decrease the distance between the lines In SVM soft classifier we tradeoff between m...

Algorithm a step-by-step procedure designed to carry out a task Change Detection Identifying when a significant change has taken place 00:32 01:24 Classification Separation of data into two or more categories Classifier A boundary that separates data into two or mo...

greedy algorithm at each step, the algorithm does the thing that looks best without taking future options into consideration; more classical variable selection methods stepwise - (forward, backward, combination) lasso elastic net 00:02 01:24 available metrics for variable...

1-norm Similar to rectilinear distance; measures the straight-line length of a vector from the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is square root(|

Support Vector Machine A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier SVM Pros/Cons Pros: It works really well with a clear margin of separation...

Building simpler models with fewer factors helps avoid which problems? A. Overfitting B. Low prediction quality C. Bias in the most important factors D. Difficulty in interpretation A. Overfitting D. Difficulty of interpretation Two main reasons to limit # of factors in a model. 1. Ove...