Introduction | iBreakDown Package | Describing an explanation | Parameters of describe() function | Adjusting nonsignificance treshold | Adjusting label of the explanation | Short descriptions | Displaying values | Displaying numbers | Distribution details | SHAP | Combining all the parameters

Introduction | ingredients Package | Feature Importance | Ceteris Paribus Profiles | Partial Dependence Profiles

Data for Titanic survival | Model for Titanic survival | Explainer for Titanic survival | Model Level Feature Importance | Feature effects | age | Partial Dependence Profiles | Conditional Dependence Profiles | Accumulated Local Effect Profiles | Instance level explanations | Profile clustering | Session info

Data Driven Smooth Tests with ddst package | II. Data Driven Smooth Tests for Selected Goodness-of-Fit Problems | 1. Data Driven Smooth Test for Uniformity | 2. Data Driven Smooth Test for Exponentiality | 3. Data Driven Smooth Tests for Normality | 3.1. Bounded Basis Functions | 3.2. Unbounded Basis Functions | 4. Data Driven Smooth Test for Extreme Value Distribution | III. Nonparametric Data Driven Smooth Tests for Comparing Distributions | 5. Data Driven Smooth Test for k-Sample Problem | 6. Data Driven Test for Stochastic Ordering | 7. Two-Sample Test Against Stochastic Dominance | 8. Data Driven Smooth Test for Upward Trend Alternative in k samples | 9. Data Driven Smooth Test for Umbrella Alternatives in k samples | Unknown peak

Data for Titanic survival | Model for Titanic survival | Explainer for Titanic survival | Break Down plot with D3 | Calculate variable attributions | Plot attributions with ggplot2 | Plot attributions with D3 | Calculate uncertainty for variable attributions | Show only top features | Force OX axis to be from 0 to 1

Simulated data | Explainer for the models | Ceteris paribus | Dependence profiles | Dependence profiles in groups | Session info

Sampling for local exploration | Fitting black box model | Fitting the explanation model