ISYE 6501

ISYE 6501 Midterm 1 EXAM QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024 True or false: In a regression tree, every leaf of the tree has a different regression model that might use different attributes, have different coefficients, etc. - ANSWER True - Each leaf's individual model is tailored to the subset of data points that follow all of the branches leading to the leaf. True or false: Tree-based approaches can be used for other models besides regression. - ANSWER True ...

ISYE 6501 - Midterm 1 Exam – Questions And Answers

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(|

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. Overfitting 2. Simplicity When is overfitting likely to happen? : When the number of factors is close to the number of data points. 2 | P a g e How does using a # of factors that is close to the numb...

What does SVM stand for? : Support Vector Machine Is written text structured or unstructured? : Unstructured 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 maximizing ___ and minimizing ___ : margin and errors If lambda gets small what gets emphasized, large margin or minimizing training error?, : Minimizing errors. What is a support vector? 2 | P a g e : A point that holds up a sh...

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. Overfitting 2. Simplicity When is overfitting likely to happen? : When the number of factors is close to the number of data points. 2 | P a g e How does using a # of factors that is close to the numb...

Descriptive Question : Questions that ask for explanations of what happened Predictive Questions : Questions that ask what's going to happen in the future Prescriptive Questions : Questions that ask what action or actions would be best General Questions : Generic questions on data analytics Modeling : A mathematical expression of a real-life situation 2 | P a g e Classification : Putting things into categories Classifiers : Separators Data Point : A single observation of inform...

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(|

What does SVM stand for? : Support Vector Machine Is written text structured or unstructured? : Unstructured 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 maximizing ___ and minimizing ___ : margin and errors If lambda gets small what gets emphasized, large margin or minimizing training error?, : Minimizing errors. What is a support vector? 2 | P a g e : A point that holds up a sh...

Classification problems are commonly solved using what model(s)? : Support Vector Machine Clustering problems are commonly solved using what model(s)? : k-means Response Prediction questions are commonly solved using what model(s)? : -ARIMA -CART -Exponential smoothing -linear regression -logistic regression -Random Forest Validation questions are commonly solved using what model(s)? : -Cross Validation 2 | P a g e Variance Estimation questions are commonly solved using what model...

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. Overfitting 2. Simplicity When is overfitting likely to happen? : When the number of factors is close to the number of data points

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. Overfitting 2. Simplicity When is overfitting likely to happen? : When the number of factors is close to the number of data points. 2 | P a g e How does using a # of factors that is close to the numb...