Udemy - Feature importance and model interpretation in Python

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[ CourseWikia.com ] Udemy - Feature importance and model interpretation in Python
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 1. Introduction.html (0.2 KB)
    • 2. What is feature importance.mp4 (41.4 MB)
    • 2. What is feature importance.srt (10.7 KB)
    2. Feature importance and model interpretation
    • 1. Models that calculate feature importance in Python.mp4 (103.1 MB)
    • 1. Models that calculate feature importance in Python.srt (19.5 KB)
    • 1.1 Importance.ipynb (13.8 KB)
    • 2. Introduction to SHAP.mp4 (53.0 MB)
    • 2. Introduction to SHAP.srt (12.9 KB)
    • 3. Using SHAP with tree-based models in Python.mp4 (126.0 MB)
    • 3. Using SHAP with tree-based models in Python.srt (23.8 KB)
    • 3.1 Shap with trees.ipynb (1.5 MB)
    • 4. Using SHAP with every model in Python.mp4 (135.4 MB)
    • 4. Using SHAP with every model in Python.srt (24.3 KB)
    • 4.1 SHAP with every model.ipynb (1.5 MB)
    3. Recursive Feature Elimination
    • 1. Introduction to RFE.mp4 (40.7 MB)
    • 1. Introduction to RFE.srt (8.7 KB)
    • 2. RFE in Python.mp4 (96.1 MB)
    • 2. RFE in Python.srt (19.5 KB)
    • 2.1 RFE.ipynb (15.6 KB)
    • Bonus Resources.txt (0.3 KB)

Description

Feature importance and model interpretation in Python



https://CourseWikia.com

MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 8 lectures (1h 45m) | Size: 524.1 MB
A practical course about feature importance and model interpretation using Python programming language and sklearn
What you'll 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

Requirements
Python programming language

Description
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.



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Udemy - Feature importance and model interpretation in Python

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598.8 MB
seeders:4
leechers:7
Udemy - Feature importance and model interpretation in Python

Torrent hash: 2A47FEB6D3C35CD898B135D0654AD1D65EC0D755