- Predictive Modeling
- Classification And Regression Tree (CART)
- Feature Engineering
- Regression Analysis
- Random Forest Algorithm
- Machine Learning
- Data Processing
- Applied Machine Learning
- Exploratory Data Analysis
- Machine Learning Algorithms
Interpretable Machine Learning Applications: Part 2
Completed by Shruti Bhanderi
January 11, 2021
1 hours (approximately)
Shruti Bhanderi's account is verified. Coursera certifies their successful completion of Interpretable Machine Learning Applications: Part 2
What you will learn
Apply Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation
Explain individual predictions being made by a trained machine learning model.
Add aspects for individual predictions in your Machine Learning applications.
Skills you will gain
