Feature importance and model interpretation in Python

Feature importance and model interpretation in Python, available at $49.99, has an average rating of 4.05, with 8 lectures, based on 11 reviews, and has 109 subscribers.

You will learn about How to calculate feature importance according to several models How to use SHAP technique to calculate feature importance of every model Recursive Feature Elimination How to apply RFE with and without cross-validation This course is ideal for individuals who are Python developers or Data Scientists or Computer engineers or Researchers or Students It is particularly useful for Python developers or Data Scientists or Computer engineers or Researchers or Students.

Enroll now: Feature importance and model interpretation in Python

Summary

Title: Feature importance and model interpretation in Python

Price: $49.99

Average Rating: 4.05

Number of Lectures: 8

Number of Published Lectures: 8

Number of Curriculum Items: 8

Number of Published Curriculum Objects: 8

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to calculate feature importance according to several models

How to use SHAP technique to calculate feature importance of every model

Recursive Feature Elimination

How to apply RFE with and without cross-validation

Who Should Attend

Python developers

Data Scientists

Computer engineers

Researchers

Students

Target Audiences

Python developers

Data Scientists

Computer engineers

Researchers

Students

In this practicalcourse, we are going to focus on feature importanceand model interpretation in supervised machine learningusing Python programming language.

Feature importance makes us better understand the information behind data and allows us to reduce the dimensionalityof 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 interpretationhelps us to correctly analyze and interpret the results of a model. A common approach for calculating model interpretation is the SHAPtechnique.

With this course, you are going to learn:

1. How to calculate feature importance according to a model

2. SHAP technique for calculating feature importance according to every model

3. 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. All the Jupyter notebooks are downloadable.

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.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: What is feature importance?

Chapter 2: Feature importance and model interpretation

Lecture 1: Models that calculate feature importance in Python

Lecture 2: Introduction to SHAP

Lecture 3: Using SHAP with tree-based models in Python

Lecture 4: Using SHAP with every model in Python

Chapter 3: Recursive Feature Elimination

Lecture 1: Introduction to RFE

Lecture 2: RFE in Python

Instructors

Gianluca Malato
Your Data Teacher

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4 stars: 2 votes

5 stars: 7 votes

Frequently Asked Questions

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