Interpretable Machine Learning Applications: Part 1
Completed by Sarah Liang
March 23, 2024
1 hours (approximately)
Sarah Liang'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
- Category: Model Training
- Category: Random Forest Algorithm
- Category: Decision Tree Learning
- Category: Data Preprocessing
- Category: Applied Machine Learning
- Category: Classification And Regression Tree (CART)
- Category: Predictive Modeling
- Category: Model Evaluation
- Category: Classification Algorithms
- Category: Responsible AI
- Category: Feature Engineering
- Category: Machine Learning
