A Data Odyssey
A Data Odyssey
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Friedman's H-statistic Python Tutorial | Artemis Package
Friedman’s h-statistic, also known as the H-stat or H-index, is a metric used to analyse interactions in a machine learning model. We apply the explainable AI (XAI) method using the artemis Python package. We also explain how to interpret the interaction heatmaps and bar plots. This includes the overall, parwise, normalised and unnormalised h-stat. These can be used to understand the percentage of a feature's effect that comes through interactions with another feature and all other features.
🚀 Free Course 🚀
Signup here: mailchi.mp/40909011987b/signup
XAI course: adataodyssey.com/courses/xai-with-python/
SHAP course: adataodyssey.com/courses/shap-with-python/
🚀 Companion article with link to code (no-paywall link): 🚀
[coming soon]
🚀 Useful playlists 🚀
XAI: th-cam.com/play/PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4.html
SHAP: th-cam.com/play/PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK.html
Algorithm fairness: th-cam.com/play/PLqDyyww9y-1Q0zWbng6vUOG1p3oReE2xS.html
🚀 Get in touch 🚀
Medium: conorosullyds.medium.com/
Threads: www.threads.net/@conorosullyds
Twitter: conorosullyDS
Website: adataodyssey.com/
🚀 Chapters 🚀
00:00 Introduction
00:41 Application with artemis
02:54 Overall H-stat
04:02 Pairwise H-stat
05:30 Unnormalized H-stat
มุมมอง: 146

วีดีโอ

Friedman's H-statistic for Analysing Interactions | Maths and Intuition15:06Friedman's H-statistic for Analysing Interactions | Maths and Intuition
Friedman's H-statistic for Analysing Interactions | Maths and Intuition
มุมมอง 1K19 ชั่วโมงที่ผ่านมา
We dive deep into Friedman's H-statistic also known as the H-stat or H-index. This is a popular explainable AI (XAI) method. It is a powerful metric for analyzing interactions between features in your machine learning model. We will: - Build intuition for the method by comparing it to PDPs and ICE Plots. - Explain the mathematics behind the pairwise, overall and unnormalised formulas. - Discuss...
Accumulated Local Effect Plots (ALEs) | Explanation & Python Code13:44Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
มุมมอง 41914 วันที่ผ่านมา
Highly correlated features can wreak havoc on your machine-learning model interpretations. To overcome this, we could rely on good feature selection. But there are still cases when a feature, although highly correlated, will provide some unique information leading to a more accurate model. So we need a method that can provide clear interpretations, even with multicollinearity. Thankfully we can...
PDPs and ICE Plots | Python Code | scikit-learn Package12:57PDPs and ICE Plots | Python Code | scikit-learn Package
PDPs and ICE Plots | Python Code | scikit-learn Package
มุมมอง 16828 วันที่ผ่านมา
Both Partial Dependence Plots (PDPs) and Individual Conditional Expectation (ICE) plots are a popular explainable AI (XAI) method. They can visualise the relationships used by a machine learning model to make predictions. In this video, we will see how to apply the methods using Python. We will use the scikit-learn package and the PartialDependenceDisplay & partial_dependence functions. We will...
Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math11:55Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math
Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math
มุมมอง 383หลายเดือนก่อน
Both Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots are used to understand and explain machine learning models. PDPs can tell us if a relationship between a model feature and target variable is linear, non-linear or if there is no relationship. Similarly, ICE plots are used to visualise interactions. Now, at first glance, these plots may look complicated. But you w...
Permutation Feature Importance from Scratch | Explanation & Python Code13:10Permutation Feature Importance from Scratch | Explanation & Python Code
Permutation Feature Importance from Scratch | Explanation & Python Code
มุมมอง 442หลายเดือนก่อน
Feature importance scores are a collection of methods all used to answer one question: which machine learning model features have contributed the most to predictions in general? Amongst all these methods, permutation feature importance is the most popular. This is due to it’s intuitive calculation and because it can be applied to any machine learning model. Understanding PFI is also an importan...
Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models8:38Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models
Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models
มุมมอง 254หลายเดือนก่อน
Model agnostic method can be used with any model. In Explainable AI (XAI), this means we can use them to interpret models without looking at their interworkings. This gives us a powerful way to interpret and explain complex black-box machine learning models. We will elaborate on this definition. We will also discuss the taxonomy of model agnostic methods for interpretability. They can be classi...
8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP13:398 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP
8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP
มุมมอง 686หลายเดือนก่อน
For data scientists, a regression summary might be all that's needed to understand a linear model. However, when explaining these models to a non-technical audience, it’s crucial to employ more digestible visual explanations. These 8 methods not only make linear regression more accessible but also enrich your analytical storytelling, making your findings resonate with any audience. We understan...
Feature Selection using Hierarchical Clustering | Python Tutorial15:55Feature Selection using Hierarchical Clustering | Python Tutorial
Feature Selection using Hierarchical Clustering | Python Tutorial
มุมมอง 1.1K2 หลายเดือนก่อน
In this comprehensive Python tutorial, we delve into feature selection for machine learning with hierarchical clustering. We guide you through the essentials of partitioning features into cohesive groups to minimize redundancy in model training. This technique is particularly important as your dataset expands, offering a structured alternative to manual grouping. What you'll learn: - The import...
8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics16:168 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics
8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics
มุมมอง 1852 หลายเดือนก่อน
Feature selection is hard! So, I explain how you can use a combination of variable clustering and feature importance to help create a shortlist. I will also explain the key factors you need to consider when selecting features. The most important are predictive power and predictor variety. But there are also other considerations including data quality and availability, feature stability, interpr...
Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features15:07Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features
Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features
มุมมอง 3872 หลายเดือนก่อน
There are many ways to capture underlying relationships in your data. Some will be easier to explain as they align with the intuition of your audience. So we should really be doing feature engineering not just for predictability but also for interpretability. We’re going to discuss how to reformulate features with the goal of interpretability. At the same time, we’re going to understand how to ...
Modelling Non-linear Relationships with Regression9:32Modelling Non-linear Relationships with Regression
Modelling Non-linear Relationships with Regression
มุมมอง 3932 หลายเดือนก่อน
This video is an advocacy for linear models. Its goal is to convince you that they should always be your first choice. Especially, if you care about model interpretability. This is because they are easier to explain, widely understood and accepted in many industries. Building them also requires you to think more critically about your problem and data. Most importantly, a well-structured linear ...
Explaining Machine Learning to a Non-technical Audience13:23Explaining Machine Learning to a Non-technical Audience
Explaining Machine Learning to a Non-technical Audience
มุมมอง 4483 หลายเดือนก่อน
An important part of a data scientist’s job is to explain machine learning model predictions. Often, the person receiving the explanation will be non-technical. If you start talking about cost functions, hyperparameters or p-values you will be met with blank stares. We need to translate these technical concepts into layman’s terms. This process can be more challenging than building the model it...
Get more out of Explainable AI (XAI): 10 Tips13:47Get more out of Explainable AI (XAI): 10 Tips
Get more out of Explainable AI (XAI): 10 Tips
มุมมอง 5153 หลายเดือนก่อน
Explainable Artificial intelligence (XAI), also known as Interpretable Machine Learning (IML), can explain complex machine learning models. But, the methods are not a golden bullet. You can’t simply fire them at black-box models and expect reasonable explanations for their inner workings. Yet, they can still provide incredible insight if used correctly. So, I give 10 tips for getting the most o...
The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories15:05The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories
The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories
มุมมอง 6003 หลายเดือนก่อน
Explainable AI (XAI), also known as interpretable machine learning (IML), can help you understand and explain your model. This has many benefits. It can help decrease harm and increase trust in machine learning. You can also gain knowledge of your dataset and tell better stories about your results. You can even improve the accuracy of your models and performance in production. We will discuss t...
Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals11:51Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals
Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals
มุมมอง 2K3 หลายเดือนก่อน
Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals
Data Science vs Science | Differences & Bridging the Gap11:09Data Science vs Science | Differences & Bridging the Gap
Data Science vs Science | Differences & Bridging the Gap
มุมมอง 2958 หลายเดือนก่อน
Data Science vs Science | Differences & Bridging the Gap
About the Channel and my Background | ML, XAI and Remote Sensing3:32About the Channel and my Background | ML, XAI and Remote Sensing
About the Channel and my Background | ML, XAI and Remote Sensing
มุมมอง 8158 หลายเดือนก่อน
About the Channel and my Background | ML, XAI and Remote Sensing
SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems12:59SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems
SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems
มุมมอง 7K9 หลายเดือนก่อน
SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems
Introduction to Algorithm Fairness | Causes, Measuring & Preventing Unfairness in Machine Learning5:46Introduction to Algorithm Fairness | Causes, Measuring & Preventing Unfairness in Machine Learning
Introduction to Algorithm Fairness | Causes, Measuring & Preventing Unfairness in Machine Learning
มุมมอง 1.3K9 หลายเดือนก่อน
Introduction to Algorithm Fairness | Causes, Measuring & Preventing Unfairness in Machine Learning
SHAP Violin and Heatmap Plots | Interpretations and New Insights5:26SHAP Violin and Heatmap Plots | Interpretations and New Insights
SHAP Violin and Heatmap Plots | Interpretations and New Insights
มุมมอง 3.8K9 หลายเดือนก่อน
SHAP Violin and Heatmap Plots | Interpretations and New Insights
Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing9:01Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing
Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing
มุมมอง 6589 หลายเดือนก่อน
Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing
Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact10:32Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact
Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact
มุมมอง 2.1K10 หลายเดือนก่อน
Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact
Exploratory Fairness Analysis | Quantifying Unfairness in Data7:47Exploratory Fairness Analysis | Quantifying Unfairness in Data
Exploratory Fairness Analysis | Quantifying Unfairness in Data
มุมมอง 76010 หลายเดือนก่อน
Exploratory Fairness Analysis | Quantifying Unfairness in Data
5 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops10:095 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops
5 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops
มุมมอง 59210 หลายเดือนก่อน
5 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops
Feature Engineering with Image Data | Aims, Techniques & Limitations9:03Feature Engineering with Image Data | Aims, Techniques & Limitations
Feature Engineering with Image Data | Aims, Techniques & Limitations
มุมมอง 729ปีที่แล้ว
Feature Engineering with Image Data | Aims, Techniques & Limitations
Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices9:36Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices
Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices
มุมมอง 1Kปีที่แล้ว
Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices
Interpretable vs Explainable Machine Learning7:07Interpretable vs Explainable Machine Learning
Interpretable vs Explainable Machine Learning
มุมมอง 15Kปีที่แล้ว
Interpretable vs Explainable Machine Learning
4 Significant Limitations of SHAP6:354 Significant Limitations of SHAP
4 Significant Limitations of SHAP
มุมมอง 8Kปีที่แล้ว
4 Significant Limitations of SHAP
Shapley Values for Machine Learning11:06Shapley Values for Machine Learning
Shapley Values for Machine Learning
มุมมอง 11Kปีที่แล้ว
Shapley Values for Machine Learning

ความคิดเห็น

  • @Felipe90820
    @Felipe90820 3 ชั่วโมงที่ผ่านมา

    Great content once again! Very helpful =)

  • @Gustavo-nn7zc
    @Gustavo-nn7zc 4 ชั่วโมงที่ผ่านมา

    Hi @adataodyssey , great video, thanks! Is there a way to use SHAP for ARIMA/SARIMA?

  • @adataodyssey
    @adataodyssey วันที่ผ่านมา

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

    • @wexwexexort
      @wexwexexort วันที่ผ่านมา

      Is SHAP course not available free, only the XAI?

    • @adataodyssey
      @adataodyssey วันที่ผ่านมา

      @@wexwexexort Hi Eredin, yes only the XAI course. You need to signup for the newsletter and then you will get a coupon sent to you :)

    • @wexwexexort
      @wexwexexort 16 ชั่วโมงที่ผ่านมา

      Thanks for the reply and the great content

    • @adataodyssey
      @adataodyssey 13 ชั่วโมงที่ผ่านมา

      No problem! If it helps, I have quite a comprehensive playlist of SHAP videos on YT: th-cam.com/play/PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK.html

  • @fupopanda
    @fupopanda 6 วันที่ผ่านมา

    Jumping between what you are explaining and yourself is distracting

    • @adataodyssey
      @adataodyssey 5 วันที่ผ่านมา

      Thanks for the feedback!

  • @wojpaw5362
    @wojpaw5362 6 วันที่ผ่านมา

    Liked, and subscribed! Amazing content keep it up ! Can you suggest 2 or 3 data sets I could test this on?

    • @adataodyssey
      @adataodyssey 6 วันที่ผ่านมา

      Thanks, Woj! You can try these. They have some interesting interactions. archive.ics.uci.edu/dataset/1/abalone www.kaggle.com/datasets/conorsully1/pdp-and-ice-plots

  • @makefly3305
    @makefly3305 8 วันที่ผ่านมา

    Hi I have a question at 5:45, wanna know based on which pattern of the plot you said the "km_driven" is less equally distributed and skewed to the left? 😄

    • @adataodyssey
      @adataodyssey 7 วันที่ผ่านมา

      I'm looking at the bars on the x-axis. This is known as a "rug plot". 10% of the dataset falls before the first bar, 20% before the second bar and so on... You can see that the bars are shifted towards the left. This means that most of the dataset has a lower km_driven value. I hope that makes sense?

  • @adataodyssey
    @adataodyssey 8 วันที่ผ่านมา

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @soniaspisak645
    @soniaspisak645 9 วันที่ผ่านมา

    Hi, I'm struggling with explaining GRU and LSTM models with SHAP. Encouraged by your videos, I am considering buying the course, but does it cover working with 3D data? Is even possible to implement SHAP and obtain reliable plots (without flattening the data) for time-series models?

    • @adataodyssey
      @adataodyssey 9 วันที่ผ่านมา

      Hi Sonia, unfortunately, the course focuses on tabular data and models like XGBoost, Random Forest and CatBoost. There is one lesson on SHAP for image data but it doesn't sound like that will help you much. If you are working with PyTorch, these articles might help you get started with applying SHAP: towardsdatascience.com/image-classification-with-pytorch-and-shap-can-you-trust-an-automated-car-4d8d12714eea?sk=b04dcbb8a09f049f605d2110b5c8d851 towardsdatascience.com/using-shap-to-debug-a-pytorch-image-regression-model-4b562ddef30d?sk=7eb3016839186f1ba2a6f1f105f8ff64

  • @santizdr
    @santizdr 13 วันที่ผ่านมา

    Best channel to dig deep into XAI. It would be great a video about the state of art of XAI applied on LLMs.

    • @adataodyssey
      @adataodyssey 11 วันที่ผ่านมา

      Thanks Santi! I will consider this however my interests are more in computer vision at the moment

  • @Brickkzz
    @Brickkzz 14 วันที่ผ่านมา

    Great recommendation from the youtube algorithm! Loving the content- keep it up!

    • @adataodyssey
      @adataodyssey 13 วันที่ผ่านมา

      Thanks Theo! Will do

  • @sirireddy3102
    @sirireddy3102 14 วันที่ผ่านมา

    I am getting error near model.fit my data has text and numeric So can you help me resolving it

  • @adataodyssey
    @adataodyssey 15 วันที่ผ่านมา

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @adataodyssey
    @adataodyssey 15 วันที่ผ่านมา

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @mahsadehghan-ws1kn
    @mahsadehghan-ws1kn 19 วันที่ผ่านมา

    Thank you so much for this awesome video. When I use this code in the #Train model section, I encounter this error. What is the solution?[17:50:59] C:\buildkite-agent\builds\buildkite-windows-cpu-autoscaling-group-i-0b3782d1791676daf-1\xgboost\xgboost-ci-windows\src\data\array_interface.h:492: Unicode-7 is not supported.

    • @adataodyssey
      @adataodyssey 18 วันที่ผ่านมา

      There could be many things going wrong. You can try creating a Python environment and downloading the XGBoost package and only the other ones necessary to train the model.

  • @abdelbaki8625
    @abdelbaki8625 23 วันที่ผ่านมา

    what is the article reference for this information i need it for my studies emergency, please

    • @adataodyssey
      @adataodyssey 21 วันที่ผ่านมา

      th-cam.com/users/redirect?event=video_description&redir_token=QUFFLUhqbktFYXFNVHVzc3NsTWpaYkc4Y3l3alZ0N3dmZ3xBQ3Jtc0trX2c3WmlOUVQwYW1USmJsaDh4YnpLV191dk5tOEdnOUtnVF9vZm5BbG8yTmRTaU56RXZNSE12Nkh2MjRITUZSLUZINUNPWmM3WFRlbnVGZWlscDFLZnFOZy1Xb0JiYm1RMnlQbVU2MEJ4R0hoUmJxMA&q=https%3A%2F%2Ftowardsdatascience.com%2Ffrom-shapley-to-shap-understanding-the-math-e7155414213b%3Fsk%3D329a1f042a0167162487f7bb3f0ffd46&v=UJeu29wq7d0

  • @TheCsePower
    @TheCsePower 23 วันที่ผ่านมา

    Would be nice if the pdp had some kind of confidence interval that varied with the feature value.

    • @adataodyssey
      @adataodyssey 21 วันที่ผ่านมา

      That's a good idea! You might be able to use the std of the prediction around each point. It would be related to the ICE plot where a point would have a larger std if not all the individual lines follow the same trend.

  • @abdelbaki8625
    @abdelbaki8625 24 วันที่ผ่านมา

    I don't understand

  • @v-ba
    @v-ba 24 วันที่ผ่านมา

    Great explanation, thank you very much

  • @shubhanshisinghms7745
    @shubhanshisinghms7745 25 วันที่ผ่านมา

    Can you make a video on how recruitment decision is made?

    • @adataodyssey
      @adataodyssey 21 วันที่ผ่านมา

      Do you mean how automated decisions are made or decisions for data scientists in general?

  • @youmustbenewhereguy
    @youmustbenewhereguy หลายเดือนก่อน

    how do you know which parameter of image manipulation that will be robust for any data will be faced in the future?

    • @adataodyssey
      @adataodyssey 21 วันที่ผ่านมา

      This is a difficult question to answer as it will depend on your problem. In general, you will need a robust dataset that includes images taken under all conditions for which the model is expected to operate. Then you can evaluate the models trained using different feature engineering methods on this dataset.

  • @adataodyssey
    @adataodyssey หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @felicebugge
    @felicebugge หลายเดือนก่อน

    Really useful , thank you

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      No problem Felice!

  • @slimeminem7402
    @slimeminem7402 หลายเดือนก่อน

    Personally, the distinction is not necessary.

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      I agree :) But I did think it was important when I first got into XAI.

  • @graziaedumabagraham5346
    @graziaedumabagraham5346 หลายเดือนก่อน

    Great content.

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      Thank you, Grazia!

  • @graziaedumabagraham5346
    @graziaedumabagraham5346 หลายเดือนก่อน

    Great content

  • @adataodyssey
    @adataodyssey หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @seanjohn6956
    @seanjohn6956 หลายเดือนก่อน

    just stick to the explanations no need for the jarring adlibs

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      That's boring...

  • @yelancho
    @yelancho หลายเดือนก่อน

    Appreciate a lot Prof Odyssey！Shaply values is now a more clear concept in my mind!

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      Thanks Ye! I'm glad you found it useful :)

  • @adataodyssey
    @adataodyssey หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @youtubeuser4878
    @youtubeuser4878 หลายเดือนก่อน

    Hello. Thanks for the tutorial. Regarding your XAI and SHAP courses, is there an order to how we should take the courses. Should we take the XAI before SHAP or vice versa. Thanks

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      No problem! It is better to take XAI first then SHAP. XAI covers more of the basics in the field and other useful model agnostic methods. But the SHAP course still gives some basics so it is not necessary to do the entire XAI course (or even any of it) if all you care about it learning SHAP :)

    • @youtubeuser4878
      @youtubeuser4878 หลายเดือนก่อน

      @@adataodysseyAwesome. Thank you.

  • @innocentjoseph9084
    @innocentjoseph9084 หลายเดือนก่อน

    Excellent explanation, just what I needed. Thank you.

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      I’m glad you found it useful, Innocent :)

  • @shazajmal9695
    @shazajmal9695 หลายเดือนก่อน

    Thanks Bruh! Great Content! Would be happy if you upload a video comparing Shap with LIME and Integrated Gradients. Its a hot topic rn in data science interviews.

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      Thanks for the suggestion! Would this be w.r.t. computer vision models and deep learning?

  • @franciscoletelier5598
    @franciscoletelier5598 หลายเดือนก่อน

    Great! always clear

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      That’s my goal!

  • @adataodyssey
    @adataodyssey หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @dantedt3931
    @dantedt3931 หลายเดือนก่อน

    This is awesome!

  • @kalebomb5018
    @kalebomb5018 หลายเดือนก่อน

    Thanks for the content on XAI and particularly SHAP, it's given me a good overview before I jump into the details. I have a sci-fi book recommendation for you: Hyperion and The Fall of Hyperion by Dan Simmons =) The first book is told from the perspective of 7 characters as they visit/revisit the planet of Hyperion that they've had dealings with in the past. Hyperion is a fringe planet in the Hegemony of Man, not connected via Farcaster, and thus a visit incurs significant time dilation. On the planet are artefacts from another intelligent force: the Time Tombs, a location with whacky time reversal effects, a 3 meter tall metallic creature covered in spikes known as the Shrike (which also has time manipulation abilities), and more. Identified as the only significant anomaly in the AI faction's predictions, everything seems to be converging on Hyperion as the Time Tombs open... Genuinely incredible read

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      Thanks! I actually just finished a book so this is good timing :)

  • @ShivSingh-zv1xw
    @ShivSingh-zv1xw หลายเดือนก่อน

    I have recently joined a course on eXplainable Artificial Intelligence (XAI) of yours and I am interested in applying the concepts of interpretability to image data while ensuring that the model's accuracy is preserved. please do create some videos on that topic.

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      You're in luck! The next course I want to create will be XAI for computer vision. So expect to see some content soon.

  • @ShivSingh-zv1xw
    @ShivSingh-zv1xw หลายเดือนก่อน

    I have recently joined a course on eXplainable Artificial Intelligence (XAI) of yours and I am interested in applying the concepts of interpretability to image data while ensuring that the model's accuracy is preserved. please do create some videos on that topic. Thank you!

  • @karthikeyapervela3230
    @karthikeyapervela3230 หลายเดือนก่อน

    Thanks, I was recently reading a post in LinkedIn how to eliminate highly correlated features with hierarchical clustering, but that was not clear but this is much better explained.

    • @adataodyssey
      @adataodyssey หลายเดือนก่อน

      Thanks Karthikeya! I'm glad you found it useful. I have another video coming out tomorrow about explaining linear models.

  • @mulusewwondieyaltaye4937
    @mulusewwondieyaltaye4937 2 หลายเดือนก่อน

    I can't access SHAP python course. Could you please give me the access

    • @adataodyssey
      @adataodyssey 2 หลายเดือนก่อน

      Hi Mulusew, the SHAP course is no longer free. But you will now get free access to my XAI course if you sign up to the newsletter

  • @arjendeniz6828
    @arjendeniz6828 2 หลายเดือนก่อน

    Thank you so much! I was stuck at a hierarchical analysis as I did not know that I need to transpose my dataframe. Great video!

    • @adataodyssey
      @adataodyssey 2 หลายเดือนก่อน

      I’m glad you found this useful ☺️

  • @adataodyssey
    @adataodyssey 2 หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @rodrigoccamargos
    @rodrigoccamargos 2 หลายเดือนก่อน

    This is the best channel so far for XAI content. Keep going!

    • @adataodyssey
      @adataodyssey 2 หลายเดือนก่อน

      Thank you! I appreciate that :D

  • @adataodyssey
    @adataodyssey 2 หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @adataodyssey
    @adataodyssey 2 หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @adataodyssey
    @adataodyssey 2 หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @adataodyssey
    @adataodyssey 2 หลายเดือนก่อน

    🚀 Free Course 🚀 Signup here: mailchi.mp/40909011987b/signup XAI course: adataodyssey.com/courses/xai-with-python/ SHAP course: adataodyssey.com/courses/shap-with-python/

  • @rajeshkalakoti2434
    @rajeshkalakoti2434 2 หลายเดือนก่อน

    can you give an example of how to plot heatmaps for a PyTorch model?

    • @adataodyssey
      @adataodyssey 2 หลายเดือนก่อน

      I will keep this in mind. I am planning to do a few tutorials using different packages --- Scikit learn, catboost, pytorch etc...

  • @satk4211
    @satk4211 2 หลายเดือนก่อน

    Excellent video ❤❤❤❤❤❤

    • @adataodyssey
      @adataodyssey 2 หลายเดือนก่อน

      Thank you ☺️ I’m glad it could help

  • @dance__break4155
    @dance__break4155 2 หลายเดือนก่อน

    whats the color of your eyes?