Feature importance and model interpretation in Python

Feature importance and model interpretation in Python

Online course

How to calculate the importance of the features and interpret models


This course is part of the Supervised Machine Learning in Python online course.

Calculating the importance of the features is mandatory for every machine learning project. It can be used, for example, to reduce the number of features of our dataset by discarding the less useful columns. Moreover, the interpretability and explainability of a model are very important parts of a machine learning pipeline, because they make the model behavior clear and help us to extract information that is hidden inside data. 

That’s why mastering feature importance and model interpretation is crucial for the success of a machine learning project.

Advantages of feature importance

  • Knowledge of our data

    Feature importance gives us a very useful knowledge about our data.

  • Dimensionality reduction

    Knowing the useless features we can reduce the dimensions of our problem, filtering our the noise and only keeping the useful information.

  • Model interpretation

    Several techniques can be used to interpret the results of a model (e.g. SHAP)

My online course

In my online course, I outline all the main topics about feature importance and model interpretation. All the lessons will be covered with practical examples in Python programming language.

These are the topics of the course:

Models that calculate feature importance

Several models calculate feature importance, like Lasso regression, tree-based models and SVM.

SHAP technique

SHAP is a technique that allows us to calculate the importance of the feature in a model-agnostic approach. 

Recursive Feature Elimination (RFE)

Once we have calculated feature importance, it’s useful to use this information for reducing the dimensions of our dataset. RFE can be used for this purpose. 

Python libraries 

Everything will be done using scikit-learn library, imblearn and matplotlib. 

What's inside the course?

The course contains:

2 hours of video lessons

Python code for each example

Discussion area to interact with the teacher and the students

Certificate of completion at the end


The course is excellent and very useful. All major subjects are complete and clear. A "must have" if you want to become a skilled data scientist


The teacher

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.com in Artificial Intelligence category.

Frequently Asked Questions

Does the course have a start and a finish date?

No. Once you enroll, you can follow the recorded video lessons when you want.

How can I pay for the course?

You can pay by Paypal or Credit card.

How can I follow the lessons?

Once you pay for your enrollment, you can access the recorded video lessons of the course when you want from your computer using this website. These videos are given in streaming, so you’ll need to connect to this website and have an Internet connection in order to watch them. After you create your account and log in, you can use the My Courses link in top of every page to see all the courses you have enrolled in.

What language will be used?

During this course, the spoken and written language is English.

I can’t afford the whole price. What can I do?

You can subscribe to the school membership. By paying a monthly or yearly fee, you’ll access this and the other courses of the school.

What if I’m not satisfied?

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.


You can join the course paying a one-time payment or joining the school membership.

30-day money back guarantee

If you are not satisfied with the course, we apply a 30-day refund policy. Just contact us within 30 days from the date of purchase to get a full refund.

One-time payment

$20 (+ VAT)
  • Complete access to the course lessons (2+ hours)
  • Access to the discussion area of each lesson
  • Python code for each example
  • 30-day refund policy

School membership

You can access all the courses of my school by subscribing to my school membership.

Go to the school membership

Local taxes (e.g. VAT) may apply

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