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Machine Learning Interpretability
Welcome
Introduction (7:43)
Course curriculum (4:32)
Course requirements (2:41)
Refer a friend program
Course material
Course material (2:02)
Download Jupyter notebooks
Download presentations
Download datasets
Interpretability in Machine Learning
Interpretability in machine learning (5:59)
Importance of interpretability (8:54)
Interpretability methods (7:44)
Global and local explanations (5:27)
Challenges to interpretability (7:54)
Making models more interpretable (7:37)
Additional reading resources
Quiz
Linear regression
Linear regression (15:10)
Linear regression model - demo (8:52)
Interpreting the coefficients (12:43)
Interpreting the coefficients - sklearn (10:50)
Interpreting the coefficients- stastsmodels (7:22)
Local explainability (7:13)
Local explainability - demo (5:53)
Linear model assumptions (5:15)
Effect of multicolinearity - demo (13:58)
Sparse models with Lasso (4:52)
Lasso: optimizing for interpretability - demo (13:35)
Advantages and limitations of linear models (3:02)
Additional reading resources
Quiz
Exercise (4:10)
Added Treat: A Movie We Recommend 🍿
Logistic regression
Logistic regression model (6:11)
Fitting the model (9:27)
Assessing the model (15:39)
Calculating the statistics - demo (8:49)
Logistic regression - demo (10:31)
Assessing the coefficients (6:24)
Assessing the coefficients - empirically (8:21)
Assessing the coefficients - bootstrapping (4:56)
Interpreting the coefficients (9:04)
Global explanations - demo (8:00)
Local explanations - demo (4:54)
Considerations (10:39)
Additional resources
Quiz
Exercise (15:15)
Extra Treat: Our Reading Suggestion 📕
Decision trees
Decision trees (5:58)
Decision tree induction (7:03)
Feature importance (13:23)
Global interpretation - demo (10:43)
Local interpretation (7:35)
Local interpretation - demo regression (8:43)
Local interpretation - classification demo (7:05)
Considerations (6:01)
Additional resources
Quiz
More Wisdom: Our Chosen Podcast Episode 🎧
Random Forests
Intro to ensemble learning (3:10)
Foundations of ensemble learning (optional) (3:12)
Bagging (2:52)
Random forest: global and local interpretation (3:25)
Global interpretation - demo (5:11)
Local interpretation - demo (7:25)
Explaining bagged models (3:13)
Explaining bagged models - demo (6:57)
Additional reading resources
Quiz
Gradient boosting machines
Intro to section (2:04)
Gradient boosting machines (9:36)
Global interpretation (7:54)
Global interpretation - sklearn (3:04)
Global interpretation - xgboost (3:35)
Global interpretation - lightGBM (3:07)
Local interpretation (8:15)
Local interpretation - sklearn (11:47)
Local interpretation - xgboost (7:21)
Local interpretation - xgb - manual (optional) (6:34)
Local interpretation - lightGBM (3:55)
Additional reading resources
Quiz
Exercise
Permutation feature importance
Permutation feature importance (5:03)
Mechanism (4:04)
Randomness (2:10)
Permutation feature importance - demo (11:14)
Considerations (5:15)
Additional reading resources
Quiz
Exercise
Partial dependence plots
Partial dependence plots (4:14)
Mechanism (6:06)
PDP - numerical features - demo (9:57)
PDP - categorical features - demo (4:37)
PDP with sklearn (3:25)
PDP with sklearn 2 (4:03)
Interaction PDP - demo (4:25)
PDP with PDPBox (8:02)
PDP with Dalex (7:37)
Considerations (3:04)
Additional reading resources
Quiz
Accumulated local effects plots
ALE plots - motivation (6:19)
ALE plots - intuition (3:59)
ALE plots - estimation (6:25)
ALE plots with pandas (12:30)
ALE plots with pyALE (4:49)
ALE plots with DALEX (4:28)
ALE plots with Alibi (4:25)
ALE plots and intervals (4:22)
Discrete and categorical variables (5:46)
2-D ALE plots (4:25)
2-D ALE plots - demo (4:44)
Additional reading resources
Quiz
Individual Condtional Expectation
Individual conditional expectation (4:32)
ICE plots uses (2:51)
ICE plots with sklearn (5:34)
ICE plots with PDPBox (4:21)
Considerations (3:33)
ICE with Dalex (3:35)
Additional reading resources
Quiz
LIME
LIME - coming soon.
Shapley Values and SHAP
Shapley values - coming soon!
LIME - coming soon.
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