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A Data Odyssey @UChsoWqJbEjBwrn00Zvghi4w@youtube.com

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Data exploration, interpretable machine learning, explainabl


20:31
Applying Permutation Channel Importance (PCI) to a Remote Sensing Model | Python Tutorial
10:13
Explaining Computer Vision Models with PCI
26:19
Explaining Anomalies with Isolation Forest and SHAP | Python Tutorial
08:41
SHAP with CatBoostClassifier for Categorical Features | Python Tutorial
09:42
Applying LIME with Python | Local & Global Interpretations
08:36
An introduction to LIME for local interpretations | Intuition and Algorithm |
08:20
Friedman's H-statistic Python Tutorial | Artemis Package
15:06
Friedman's H-statistic for Analysing Interactions | Maths and Intuition
13:44
Accumulated Local Effect Plots (ALEs) | Explanation & Python Code
12:57
PDPs and ICE Plots | Python Code | scikit-learn Package
11:55
Partial Dependence (PDPs) and Individual Conditional Expectation (ICE) Plots | Intuition and Math
13:10
Permutation Feature Importance from Scratch | Explanation & Python Code
08:38
Model Agnostic Methods for XAI | Global v.s. Local | Permutation v.s. Surrogate Models
13:39
8 Plots for Explaining Linear Regression | Residuals, Weight, Effect & SHAP
15:55
Feature Selection using Hierarchical Clustering | Python Tutorial
16:16
8 Characteristics of a Good Machine Learning Feature | Predictive, Variety, Interpretability, Ethics
15:07
Interpretable Feature Engineering | How to Build Intuitive Machine Learning Features
09:32
Modelling Non-linear Relationships with Regression
13:23
Explaining Machine Learning to a Non-technical Audience
13:47
Get more out of Explainable AI (XAI): 10 Tips
15:05
The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories
11:51
Introduction to Explainable AI (XAI) | Interpretable models, agnostic methods, counterfactuals
11:09
Data Science vs Science | Differences & Bridging the Gap
03:32
About the Channel and my Background | ML, XAI and Remote Sensing
12:59
SHAP for Binary and Multiclass Target Variables | Code and Explanations for Classification Problems
05:46
Introduction to Algorithm Fairness | Causes, Measuring & Preventing Unfairness in Machine Learning
05:26
SHAP Violin and Heatmap Plots | Interpretations and New Insights
09:01
Correcting Unfairness in Machine Learning | Pre-processing, In-processing, Post-processing
10:32
Definitions of Fairness in Machine Learning | Equal Opportunity, Equalized Odds & Disparate Impact
07:47
Exploratory Fairness Analysis | Quantifying Unfairness in Data
10:09
5 Reasons for Unfair Models | Proxy Variables, Unbalanced Samples & Negative Feedback Loops
09:03
Feature Engineering with Image Data | Aims, Techniques & Limitations
09:36
Image Augmentation for Deep Learning | Benefits, Techniques & Best Practices
07:07
Interpretable vs Explainable Machine Learning
06:35
4 Significant Limitations of SHAP
11:06
Shapley Values for Machine Learning
11:48
The mathematics behind Shapley Values
15:41
SHAP with Python (Code and Explanations)
07:07
SHAP values for beginners | What they mean and their applications
03:02
5 ways to use a Seaborn Heatmap
05:11
Data Exploration with PCA