Feature importance and model interpretation in Python
A practical course about feature importance and model interpretation using Python programming language and sklearn
Updated on Sep, 2023
Language - English
In this practical course, we are going to focus on feature importance and model interpretation in supervised machine learning using Python programming language.
Feature importance makes us better understand the information behind data and allows us to reduce the dimensionality of our problem considering only the relevant information, discarding all the useless variables. A common dimensionality reduction technique based on feature importance is the Recursive Feature Elimination.
Model interpretation helps us to correctly analyze and interpret the results of a model. A common approach for calculating model interpretation is the SHAP technique.
With this course, you are going to learn:
How to calculate feature importance according to a model
SHAP technique for calculating feature importance according to every model
Recursive Feature Elimination for dimensionality reduction, with and without the use of cross-validation
All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry.
This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the larger course.
What will you learn in this course:
- How to calculate the importance of the features
- How to calculate the impact of a feature according to a model
- How to reduce the number of the features of a dataset according to their importance
What are the prerequisites for this course?
- Python programming language.
Check out the detailed breakdown of what’s inside the course
- What is feature importance? 08:37 08:37
Feature importance and model interpretation
Recursive Feature Elimination
My name is Gianluca Malato, I'm Italian and have a Master's Degree cum laude in Theoretical Physics of disordered systems at "La Sapienza" University of Rome.
I'm a Data Scientist who has been working for years in the banking and insurance sector. I have extensive experience in software programming and project management and I have been dealing with data analysis and machine learning in the corporate environment for several years.
I am also skilled in data analysis (e.g. relational databases and SQL language), numerical algorithms (e.g. ODE integration, optimization algorithtms) and simulation (e.g. Monte Carlo techniques).
I've written many articles about Machine Learning, R and Python and I've been a Top Writer on Medium in Artificial Intelligence category.
User your certification to make a career change or to advance in your current career. Salaries are among the highest in the world.
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