- Responsible AI
- Predictive Modeling
- Predictive Analytics
- Feature Engineering
- Random Forest Algorithm
- Applied Machine Learning
- Classification And Regression Tree (CART)
- Machine Learning
- Data Import/Export
- Decision Tree Learning
Interpretable Machine Learning Applications: Part 1
Completed by Shruti Bhanderi
January 8, 2021
1 hours (approximately)
Shruti Bhanderi's account is verified. Coursera certifies their successful completion of Interpretable Machine Learning Applications: Part 1
What you will learn
How to select and compare different prediction models (classification regressors) for a real world dataset (FIFA 2018 Soccer World Cup Statistics).
How to extract the most important features, which impact the classifiers, in a model-agnostic approach, together with caveats.
How to get an insight into the way values of the most important features impact the predictions made by the classifiers.
Skills you will gain
