Sumanta Basu is an Assistant Professor in the Department of Statistics and Data Science at Cornell University. Broadly, his research interests are structure learning and the prediction of large systems from data, with a particular emphasis on developing learning algorithms for time series data. Professor Basu also collaborates with biological and social scientists on a wide range of problems, including genomics, large-scale metabolomics, and systemic risk monitoring in financial markets. His research is supported by multiple awards from the National Science Foundation and the National Institutes of Health. At Cornell, Professor Basu teaches “Introductory Statistics” for graduate students outside the Statistics Department and “Computational Statistics” for Statistics Ph.D. students. He also serves as a faculty consultant at Cornell Statistical Consulting Unit, which assists the broader Cornell community with various aspects of analyzing empirical research. Professor Basu received his Ph.D. from the University of Michigan and was a postdoctoral scholar at the University of California, Berkeley, and Lawrence Berkeley National Laboratory. Before he received his Ph.D, Professor Basu was a business analyst, working with large retail companies on the design and data analysis of their promotional campaigns.
Course Overview
When working with real-world datasets, more than a single model may be required to capture the complexity of the data. Ensemble methods prove to be extremely useful with complex datasets by allowing us to combine simpler models to fully grasp the patterns in the data, thereby improving the predictive power of the models.
In this course, you'll discover how to use two ensemble methods: random forests and boosted decision trees. You'll practice these ensemble methods with datasets in R and apply the ensemble techniques you've learned to build robust predictive models. You'll practice improving decision tree performance using random forest models and practice interpreting those models. You'll then use another technique and apply boosting to reduce errors and aggregate predictions to decision trees.
You are required to have completed the following courses or have equivalent experience before taking this course:
- Nonlinear Regression Models
- Modeling Interactions Between Predictors
- Foundations of Predictive Modeling
Key Course Takeaways
- Identify the limitations of decision trees
- Fit random forest models and practice implementing them in R
- Use mean decrease in accuracy (MDA), mean decrease in impurity (MDI), and partial dependence plots to interpret "black box" ensemble methods
- Apply the basics of boosting to classification trees and implement with datasets in R
How It Works
Course Author
Who Should Enroll
- Current and aspiring data scientists and analysts
- Business decision makers
- Marketing analysts
- Consultants
- Executives
- Anyone seeking to gain deeper exposure to data science
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