1
Homework
Module 1
1. Explain whether each scenario is a classification or regression problem, and indicate whether we are most interested in inference or prediction. Finally, provide the number of data points,
\(n\)
, and the number of predictors,
\(p\)
. (3 points)
2. Describe the differences between a parametric and a non-parametric statistical learning approach. (2 points)
3. Explore the dataset
ISLR2::Boston
. (11 points)
Module 2
1. Flexible vs Inflexible Methods (2 points)
2. Predicting Airfare on New Routes
Module 3
1. Differences between LDA and QDA. (4 points)
2. NASA: Asteroid classification
3. Handling class imbalance in classification problems (4 points)
Module 4
1. Diabetes dataset
2. Estimate model performance using bootstrap
Module 5
1. Build elasticnet model for predicting airfare prices (L1/L2 regularization)
2. NASA: Asteroid classification - classification with dimensionality reduction
Module 6
1. Predict out of state tuition (feature selection)
2. Predict out of state tuition (GAM model)
Module 7
1. Predicting Prices of Used Cars (Regression Trees)
Module 8
1. Predicting fish toxicity of chemicals (regression)
Module 9
1. Sentiment analysis using SVM
Module 10
1. Analyzing the ANES 2022 Pilot Study - PCA
2. Analyzing the ANES 2022 Pilot Study - Clustering
DS-6030 Homework Module 10
DS-6030 Homework Module 10
Homework
Peter Gedeck
2025-05-10
1
Homework