Course curriculum | Train in Data

This site uses cookies

Cookies are important for this site to function properly, to guarantee your safety, and to provide you with the best experience. By continuing to browse, you agree to the use of these cookies.
Learn More.

Autoplay
Autocomplete

Previous Lesson Complete and Continue

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
How did you hear about us?

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)
How are we doing? (0:26)
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)
How are we doing? (0:24)
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)
How are we doing? (0:24)
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

Surrogate models

Surrogate models (5:13)
Considerations (7:18)
Explaining kmeans with a surrogate (19:11)
Explaining anomaly detection with a surrogate (16:50)
Additional reading resources
Quiz

LIME

LIME - motivation (3:54)
LIME - mechanism (7:58)
LIME - surrogate (7:49)
Synthetic data for images and text (5:58)
Synthetic tabular data (7:05)
Distance calculation (4:54)
Interpreting tabular data with the LIME library (14:51)
Interpreting tabular data with LIME manually (7:13)
Interpreting data with LIME through Dalex (3:11)
Interpreting texts with LIME (12:51)
Interpreting images with LIME (8:18)
Considerations (6:34)
Additional reading resources
Quiz

Shapley Values and SHAP

SHAP - motivation (6:22)
Game theory - intuition (6:50)
Shapley value (9:54)
Shapley value calculation (12:51)
Shapley value calculation - demo (17:50)
Sampling values (Monte Carlo) (9:11)
Exact Explainer (7:47)
Exact Explainer - demo (13:33)
Permutation (12:26)
Permutation Explainer - demo (9:33)
Sampling Explainer (4:25)
SHAP Kernel (9:52)
SHAP estimation for linear models (8:42)
SHAP for linear regression - demo (5:38)
SHAP for logistic regression (6:04)
SHAP for logistic regression - demo (6:04)
SHAP for decision tree-based models (13:52)
TreeSHAP - demo (5:09)
Additional reading resources
Quiz

Congratulations! You did it!

Congratulations
Next steps

Course curriculum

Complete and Continue

Discussion

5 comments