A Data Odyssey
A Data Odyssey
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Friedman's H-statistic for Analysing Interactions | Maths and Intuition
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 its limitations including computational complexity and spurious interactions caused by multicollinearity.
We will see that there are two versions of the statistic. One for the interaction between two features and another for the interaction between a feature and all the other features. We will also explore the difference between the normalized and unnormalized H-stat. We will understand how these all work together to find and analyse interactions in your data and models.
🚀 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): 🚀
medium.com/towards-data-science/understanding-freidmans-h-statistic-h-stat-for-interactions-43fb5e31a586?sk=33c8f3eee9106d35d069f39077e7fcf9
🚀 Useful playlists 🚀
XAI: czcams.com/play/PLqDyyww9y-1SwNZ-6CmvfXDAOdLS7yUQ4.html
SHAP: czcams.com/play/PLqDyyww9y-1SJgMw92x90qPYpHgahDLIK.html
Algorithm fairness: czcams.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
01:20 Definition of an interaction
02:58 Intuition
05:18 Mathematics
07:50 Pairwise H-stat
09:35 Overall H-stat
11:10 Limitations
zhlédnutí: 748

Video

Accumulated Local Effect Plots (ALEs) | Explanation & Python Code13:44Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
zhlédnutí 406Před dnem
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
zhlédnutí 163Před 21 dnem
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
zhlédnutí 360Před měsícem
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
zhlédnutí 433Před měsícem
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
zhlédnutí 252Před měsícem
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
zhlédnutí 675Před měsícem
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
zhlédnutí 1,1KPřed 2 měsíci
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
zhlédnutí 180Před 2 měsíci
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
zhlédnutí 382Před 2 měsíci
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
zhlédnutí 386Před 2 měsíci
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
zhlédnutí 443Před 2 měsíci
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
zhlédnutí 507Před 3 měsíci
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
zhlédnutí 578Před 3 měsíci
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
zhlédnutí 1,9KPřed 3 měsíci
Artificial intelligence (AI) and machine learning (ML) impact our lives in many ways. From mundane tasks to critical decision-making processes, AI's role is becoming more central. As a result, the need for transparency and interpretability of these systems is growing. This is why we need to field of Expianable AI (XAI) also known as interpretable machine learning (IML). We will take a brief loo...
Data Science vs Science | Differences & Bridging the Gap11:09Data Science vs Science | Differences & Bridging the Gap
Data Science vs Science | Differences & Bridging the Gap
zhlédnutí 295Před 8 měsíci
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
zhlédnutí 806Před 8 měsíci
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
zhlédnutí 7KPřed 8 měsíci
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
zhlédnutí 1,2KPřed 9 měsíci
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
zhlédnutí 3,8KPřed 9 měsíci
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
zhlédnutí 649Před 9 měsíci
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
zhlédnutí 2KPřed 10 měsíci
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
zhlédnutí 749Před 10 měsíci
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
zhlédnutí 583Před 10 měsíci
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
zhlédnutí 721Před rokem
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
zhlédnutí 1KPřed rokem
Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices
Interpretable vs Explainable Machine Learning7:07Interpretable vs Explainable Machine Learning
Interpretable vs Explainable Machine Learning
zhlédnutí 15KPřed rokem
Interpretable vs Explainable Machine Learning
4 Significant Limitations of SHAP6:354 Significant Limitations of SHAP
4 Significant Limitations of SHAP
zhlédnutí 8KPřed rokem
4 Significant Limitations of SHAP
Shapley Values for Machine Learning11:06Shapley Values for Machine Learning
Shapley Values for Machine Learning
zhlédnutí 11KPřed rokem
Shapley Values for Machine Learning
The mathematics behind Shapley Values11:48The mathematics behind Shapley Values
The mathematics behind Shapley Values
zhlédnutí 19KPřed rokem
The mathematics behind Shapley Values

Komentáře

  • @fupopanda
    @fupopanda Před 3 dny

    Jumping between what you are explaining and yourself is distracting

  • @wojpaw5362
    @wojpaw5362 Před 3 dny

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

    • @adataodyssey
      @adataodyssey Před 3 dny

      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 Před 5 dny

    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 Před 4 dny

      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 Před 5 dny

    🚀 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 Před 6 dny

    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 Před 6 dny

      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 Před 10 dny

    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 Před 8 dny

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

  • @Brickkzz
    @Brickkzz Před 11 dny

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

  • @sirireddy3102
    @sirireddy3102 Před 11 dny

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

  • @adataodyssey
    @adataodyssey Před 12 dny

    🚀 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 Před 12 dny

    🚀 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 Před 16 dny

    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 Před 15 dny

      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 Před 20 dny

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

    • @adataodyssey
      @adataodyssey Před 18 dny

      czcams.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 Před 20 dny

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

    • @adataodyssey
      @adataodyssey Před 18 dny

      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 Před 21 dnem

    I don't understand

  • @v-ba
    @v-ba Před 21 dnem

    Great explanation, thank you very much

  • @shubhanshisinghms7745
    @shubhanshisinghms7745 Před 22 dny

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

    • @adataodyssey
      @adataodyssey Před 18 dny

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

  • @youmustbenewhereguy
    @youmustbenewhereguy Před 27 dny

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

    • @adataodyssey
      @adataodyssey Před 18 dny

      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 Před měsícem

    🚀 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 Před měsícem

    Really useful , thank you

  • @slimeminem7402
    @slimeminem7402 Před měsícem

    Personally, the distinction is not necessary.

    • @adataodyssey
      @adataodyssey Před měsícem

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

  • @graziaedumabagraham5346
    @graziaedumabagraham5346 Před měsícem

    Great content.

  • @graziaedumabagraham5346
    @graziaedumabagraham5346 Před měsícem

    Great content

  • @adataodyssey
    @adataodyssey Před měsícem

    🚀 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 Před měsícem

    just stick to the explanations no need for the jarring adlibs

  • @yelancho
    @yelancho Před měsícem

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

    • @adataodyssey
      @adataodyssey Před měsícem

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

  • @adataodyssey
    @adataodyssey Před měsícem

    🚀 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 Před měsícem

    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 Před měsícem

      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 Před měsícem

      @@adataodysseyAwesome. Thank you.

  • @innocentjoseph9084
    @innocentjoseph9084 Před měsícem

    Excellent explanation, just what I needed. Thank you.

    • @adataodyssey
      @adataodyssey Před měsícem

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

  • @shazajmal9695
    @shazajmal9695 Před měsícem

    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 Před měsícem

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

  • @franciscoletelier5598
    @franciscoletelier5598 Před měsícem

    Great! always clear

  • @adataodyssey
    @adataodyssey Před měsícem

    🚀 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 Před měsícem

    This is awesome!

  • @kalebomb5018
    @kalebomb5018 Před měsícem

    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 Před měsícem

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

  • @ShivSingh-zv1xw
    @ShivSingh-zv1xw Před měsícem

    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 Před měsícem

      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 Před měsícem

    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 Před měsícem

    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 Před měsícem

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

  • @mulusewwondieyaltaye4937
    @mulusewwondieyaltaye4937 Před měsícem

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

    • @adataodyssey
      @adataodyssey Před měsícem

      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 Před měsícem

    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 Před měsícem

      I’m glad you found this useful ☺️

  • @adataodyssey
    @adataodyssey Před 2 měsíci

    🚀 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 Před 2 měsíci

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

  • @adataodyssey
    @adataodyssey Před 2 měsíci

    🚀 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 Před 2 měsíci

    🚀 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 Před 2 měsíci

    🚀 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 Před 2 měsíci

    🚀 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 Před 2 měsíci

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

    • @adataodyssey
      @adataodyssey Před 2 měsíci

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

  • @satk4211
    @satk4211 Před 2 měsíci

    Excellent video ❤❤❤❤❤❤

    • @adataodyssey
      @adataodyssey Před 2 měsíci

      Thank you ☺️ I’m glad it could help

  • @dance__break4155
    @dance__break4155 Před 2 měsíci

    whats the color of your eyes?

  • @silver_soul98
    @silver_soul98 Před 2 měsíci

    Bro that was a nice explanation. thanks so much.

    • @adataodyssey
      @adataodyssey Před 2 měsíci

      No problem :) I’m glad it was useful

  • @Hoxle-87
    @Hoxle-87 Před 2 měsíci

    Excellent! You gave me an idea 💡 Great job!

    • @adataodyssey
      @adataodyssey Před 2 měsíci

      Thanks Jose! I'm glad I could help

  • @cyberlando
    @cyberlando Před 2 měsíci

    Where is the link for the code for the insurance model?

    • @adataodyssey
      @adataodyssey Před 2 měsíci

      github.com/a-data-odyssey/XAI-tutorial/blob/main/src/intro/human_friendly_explanations.ipynb