How to Interpret Machine Learning Models using SHAP in Python | Python Project Tutorial | Part 1

AI Probably is all about Artificial Intelligence, Machine Learning, Natural Language Processing and Python Programming. Check out our page for fun-filled informational content.

In this tutorial, we will guide through theory about what is Shapely Additive Explanation (SHAP). SHAP is a neural network used for interpretation of predictions made by complex machine learning models. It is a neural system which opens up about the black box which occurs in machine learning.

Here we will demonstrate how to apply these techniques in Python on a real-world data science problem using an example of Melbourne Housing Market Dataset.
In this project-based video you will get insights about how to install SHAP, SHAP graphs and SHAP values using demos.

This video is for a beginner in data science to an advanced level programmer

The Code:

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#SHAP #Python #Programming #Machine Learning
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Along with the video, you will learn:

01:44 - Neural Network: BlackBox?
03:21 - What happens in Neural Network
03:25 - Hidden Layers
04:12 - SHAP(Shapley Additive exPlanations)
04:24 - SHAP Values: Opening the BlackBox
06:06 - Installing SHAP
06:28 - SHAP Explainers
07:21 - SHAP Graphs
07:47 - Demo 1: SHAP Values on Regression Problem

[lavinnasays]

Good video. I hope your channel gets more subscribers. If you looking for a teammate in Kaggle competition please let me know.

[pattiknuth]

Video would have been a lot better if they used someone who could speak english decently.