Explainability Blog with Detail Code Notebook With Visualization

Here is detail code & notebook that describe all methods for explainability. The method include DALEX, LIME, BREAKDOWN, Shapley. Using this one can describe how different feature and feature interaction impact target variable. This example predict wine quality based on wine features.
R Notebook

Goal: Explain which features make a product good quality.

Example Question : What properties of wine makes a good wine.

Wine has been used and produced for thousands of years. Different culture, different age group enjoy drinking wine. There is 400B of market cap. Companies across the world are competing to produce better quality wine to get market share.

However there is no consensus what is definition of good quality wine. Good quality is hard to define in words and explain. To explain good quality wine, we study
a wine dataset Built explanatory model

Dataset

“Wine Quality” dataset from the UC Irvine Machine Learning Data Repository

#Tutorial
#install.packages("ggridges")
#install.packages("ggthemes")
#install.packages("iml")
#install.packages("breakDown")
#install.packages("DALEX")
#install.packages("glmnet")
#install.packages("partykit")
# data wrangling
library(tidyverse)
library(readr)

# ml
library(caret)
## Loading required package: lattice
## 
## Attaching package: 'caret'
## The following objects are masked from 'package:MLmetrics':
## 
##     MAE, RMSE
## The following object is masked from 'package:purrr':
## 
##     lift
# plotting
library(gridExtra)
library(grid)
library(ggridges)
library(ggthemes)
theme_set(theme_minimal())

# explaining models
# https://github.com/christophM/iml
library(iml)

# https://pbiecek.github.io/breakDown/
library(breakDown)

# https://pbiecek.github.io/DALEX/
library(DALEX)
## Welcome to DALEX (version: 2.3.0).
## Find examples and detailed introduction at: http://ema.drwhy.ai/
## Additional features will be available after installation of: ggpubr.
## Use 'install_dependencies()' to get all suggested dependencies
## 
## Attaching package: 'DALEX'
## The following object is masked from 'package:dplyr':
## 
##     explain
library(partykit)
## Loading required package: libcoin
## Loading required package: mvtnorm
library(libcoin)
library(mvtnorm)

Overview

We first load data, clean, do data exploration. Build linear regression model. Build random forst predictor Explain

  1. Feature importance
  2. Partial dependence plots
  3. Individual conditional expectation plots (ICE)
  4. Tree surrogate
  5. LocalModel: Local Interpretable Model-agnostic Explanations (similar to lime)
  6. Shapley value for explaining single predictions

Load the data

# Load and clean data
clean_data <- function(df){
  red_wine_df <- read_delim("data/winequality-red.csv", delim=";")
  red_wine_df['wine_type'] <- 'red'
  
  white_wine_df <- read_delim("data/winequality-white.csv", delim=";")
  white_wine_df['wine_type'] <- 'white'
  
  wine_df <- bind_rows(red_wine_df,white_wine_df) %>% 
    filter(quality >= 0 & quality <= 10) %>% 
    drop_na()
  
  #white_wine_df <- read_delim("data/winequality-white.csv", delim=";")
  #white_wine_df['wine_type'] <- 'white'
  
  #wine_df <- rbind(red_wine_df,white_wine_df) %>% 

  return(wine_df)
}

wine_df <- clean_data(df)
## Rows: 1599 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## dbl (12): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Rows: 4898 Columns: 12
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## dbl (12): fixed acidity, volatile acidity, citric acid, residual sugar, chlo...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
wine_df = wine_df %>%
            mutate(quality_cat = as.factor(ifelse(quality < 6, "qual_low", "qual_high")))

Data Exploration

“Table 1” shows all the variable of our data along with the first few rows of our data. The varaibles are defined as follows:

  • fixed_acidity - acids involved with wine that are fixed (don’t evaporate readily)

  • volatile_acidity - the amount of acetic acid in wine, which at high of levels can lead to an unpleasant, vinegar taste

  • citric_acid - weak organic acid that occurs naturally in citrus fruits and can add ‘freshness’ and flavor to wines

  • residual_sugar - refers to any natural grape sugars that are left over after fermentation stops. it’s rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet

  • chlorides - the amount of salt in the wine

  • free_sulfur_dioxide -free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulphate ion. It exhibits both germicidal and antioxidant properties

  • total_sulfur_dioxide - amount of free and bound forms of S02

  • density - self explanatory

  • pH - from a winemaker’s point of view, it is a way to measure ripeness in relation to acidity

  • sulphates - a wine additive which can contribute to sulfur dioxide gas (S02) levels. It acts as antimicrobial and antioxidant

  • alcohol - the percent alcohol content of the wine

  • quality - output variable

Out of 6497 rows in dataset, 6251 are clean rows. We used data 1875 for exploration and 4376 for testing/model.

colnames(wine_df) = gsub(" ", "_", colnames(wine_df))
glimpse(wine_df)
## Rows: 6,497
## Columns: 14
## $ fixed_acidity        <dbl> 7.4, 7.8, 7.8, 11.2, 7.4, 7.4, 7.9, 7.3, 7.8, 7.5…
## $ volatile_acidity     <dbl> 0.700, 0.880, 0.760, 0.280, 0.700, 0.660, 0.600, …
## $ citric_acid          <dbl> 0.00, 0.00, 0.04, 0.56, 0.00, 0.00, 0.06, 0.00, 0…
## $ residual_sugar       <dbl> 1.9, 2.6, 2.3, 1.9, 1.9, 1.8, 1.6, 1.2, 2.0, 6.1,…
## $ chlorides            <dbl> 0.076, 0.098, 0.092, 0.075, 0.076, 0.075, 0.069, …
## $ free_sulfur_dioxide  <dbl> 11, 25, 15, 17, 11, 13, 15, 15, 9, 17, 15, 17, 16…
## $ total_sulfur_dioxide <dbl> 34, 67, 54, 60, 34, 40, 59, 21, 18, 102, 65, 102,…
## $ density              <dbl> 0.9978, 0.9968, 0.9970, 0.9980, 0.9978, 0.9978, 0…
## $ pH                   <dbl> 3.51, 3.20, 3.26, 3.16, 3.51, 3.51, 3.30, 3.39, 3…
## $ sulphates            <dbl> 0.56, 0.68, 0.65, 0.58, 0.56, 0.56, 0.46, 0.47, 0…
## $ alcohol              <dbl> 9.4, 9.8, 9.8, 9.8, 9.4, 9.4, 9.4, 10.0, 9.5, 10.…
## $ quality              <dbl> 5, 5, 5, 6, 5, 5, 5, 7, 7, 5, 5, 5, 5, 5, 5, 5, 7…
## $ wine_type            <chr> "red", "red", "red", "red", "red", "red", "red", …
## $ quality_cat          <fct> qual_low, qual_low, qual_low, qual_high, qual_low…
p1 = wine_df %>%
  ggplot(aes(x = quality, fill = quality)) +
    geom_bar(alpha = 0.8) +
    scale_fill_tableau() +
    guides(fill = FALSE)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
p1

p2 = wine_df %>%
  ggplot(aes(x = quality_cat, fill = quality_cat)) +
    geom_bar(alpha = 0.8) +
    scale_fill_tableau() +
    guides(fill = FALSE)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
p2

p3 = wine_df %>%
  gather(x, y, fixed_acidity:alcohol) %>%
  ggplot(aes(x = y, y = quality_cat, color = quality_cat, fill = quality_cat)) +
    facet_wrap( ~ x, scale = "free", ncol = 4) +
    scale_fill_tableau() +
    scale_color_tableau() +
    scale_fill_viridis_d(direction = -1, guide = "none")+
    geom_density_ridges(alpha = 0.7) +
    guides(fill = FALSE, color = FALSE) +
    theme(plot.title = element_text(size = 24, hjust = 0.5))+
    labs(title = "Relationship between Quality and and Features ", y = "Quality")
## Scale for 'fill' is already present. Adding another scale for 'fill', which
## will replace the existing scale.
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.
p3
## Picking joint bandwidth of 0.182
## Picking joint bandwidth of 0.00375
## Picking joint bandwidth of 0.0211
## Picking joint bandwidth of 0.000499
## Picking joint bandwidth of 0.168
## Picking joint bandwidth of 3.28
## Picking joint bandwidth of 0.0278
## Picking joint bandwidth of 0.855
## Picking joint bandwidth of 0.0214
## Picking joint bandwidth of 10.2
## Picking joint bandwidth of 0.0266

#grid.arrange(p1, p2, ncol = 2, widths = c(0.3, 0.7))
wine_df2 <- wine_df[c ('fixed_acidity' ,'volatile_acidity','citric_acid', 
                       'residual_sugar', 'chlorides','free_sulfur_dioxide', 
                       'total_sulfur_dioxide', 'density',
                      'pH', 'sulphates','alcohol', 'quality_cat' )]
glimpse(wine_df2)
## Rows: 6,497
## Columns: 12
## $ fixed_acidity        <dbl> 7.4, 7.8, 7.8, 11.2, 7.4, 7.4, 7.9, 7.3, 7.8, 7.5…
## $ volatile_acidity     <dbl> 0.700, 0.880, 0.760, 0.280, 0.700, 0.660, 0.600, …
## $ citric_acid          <dbl> 0.00, 0.00, 0.04, 0.56, 0.00, 0.00, 0.06, 0.00, 0…
## $ residual_sugar       <dbl> 1.9, 2.6, 2.3, 1.9, 1.9, 1.8, 1.6, 1.2, 2.0, 6.1,…
## $ chlorides            <dbl> 0.076, 0.098, 0.092, 0.075, 0.076, 0.075, 0.069, …
## $ free_sulfur_dioxide  <dbl> 11, 25, 15, 17, 11, 13, 15, 15, 9, 17, 15, 17, 16…
## $ total_sulfur_dioxide <dbl> 34, 67, 54, 60, 34, 40, 59, 21, 18, 102, 65, 102,…
## $ density              <dbl> 0.9978, 0.9968, 0.9970, 0.9980, 0.9978, 0.9978, 0…
## $ pH                   <dbl> 3.51, 3.20, 3.26, 3.16, 3.51, 3.51, 3.30, 3.39, 3…
## $ sulphates            <dbl> 0.56, 0.68, 0.65, 0.58, 0.56, 0.56, 0.46, 0.47, 0…
## $ alcohol              <dbl> 9.4, 9.8, 9.8, 9.8, 9.4, 9.4, 9.4, 10.0, 9.5, 10.…
## $ quality_cat          <fct> qual_low, qual_low, qual_low, qual_high, qual_low…
#Remove categorical columns
wine_df_num = subset(wine_df2, select = -c(quality_cat))
histgrams <- apply(wine_df_num, 2,
                   function(x){
                       figure(title= "NULL", xlab = colnames(x), 
                              width = 400, height = 250) %>%
                       ly_hist(x,breaks = 40, freq = FALSE, 
                               color=brewer.pal(9, "GnBu")) %>%
                       ly_density(x)})

grid_plot(histgrams, nrow=6)

Build Model

Build Train and test set

set.seed(42)

idx = createDataPartition(wine_df2$quality_cat, 
                           p = 0.7, 
                           list = FALSE, 
                           times = 1)

wine_train = wine_df2[ idx,]
wine_test  = wine_df2[-idx,]
glimpse(wine_df2)
## Rows: 6,497
## Columns: 12
## $ fixed_acidity        <dbl> 7.4, 7.8, 7.8, 11.2, 7.4, 7.4, 7.9, 7.3, 7.8, 7.5…
## $ volatile_acidity     <dbl> 0.700, 0.880, 0.760, 0.280, 0.700, 0.660, 0.600, …
## $ citric_acid          <dbl> 0.00, 0.00, 0.04, 0.56, 0.00, 0.00, 0.06, 0.00, 0…
## $ residual_sugar       <dbl> 1.9, 2.6, 2.3, 1.9, 1.9, 1.8, 1.6, 1.2, 2.0, 6.1,…
## $ chlorides            <dbl> 0.076, 0.098, 0.092, 0.075, 0.076, 0.075, 0.069, …
## $ free_sulfur_dioxide  <dbl> 11, 25, 15, 17, 11, 13, 15, 15, 9, 17, 15, 17, 16…
## $ total_sulfur_dioxide <dbl> 34, 67, 54, 60, 34, 40, 59, 21, 18, 102, 65, 102,…
## $ density              <dbl> 0.9978, 0.9968, 0.9970, 0.9980, 0.9978, 0.9978, 0…
## $ pH                   <dbl> 3.51, 3.20, 3.26, 3.16, 3.51, 3.51, 3.30, 3.39, 3…
## $ sulphates            <dbl> 0.56, 0.68, 0.65, 0.58, 0.56, 0.56, 0.46, 0.47, 0…
## $ alcohol              <dbl> 9.4, 9.8, 9.8, 9.8, 9.4, 9.4, 9.4, 10.0, 9.5, 10.…
## $ quality_cat          <fct> qual_low, qual_low, qual_low, qual_high, qual_low…
options(knitr.table.format = "latex")
head(wine_df2) %>%
  kbl(caption = "Summary Table of Wine Dataset") %>% 
  kable_classic(html_font = "Cambria", full_width = F)  %>%
  kable_styling(latex_options = c("striped", "scale_down"))
Summary Table of Wine Dataset
fixed_acidity volatile_acidity citric_acid residual_sugar chlorides free_sulfur_dioxide total_sulfur_dioxide density pH sulphates alcohol quality_cat
7.4 0.70 0.00 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 qual_low
7.8 0.88 0.00 2.6 0.098 25 67 0.9968 3.20 0.68 9.8 qual_low
7.8 0.76 0.04 2.3 0.092 15 54 0.9970 3.26 0.65 9.8 qual_low
11.2 0.28 0.56 1.9 0.075 17 60 0.9980 3.16 0.58 9.8 qual_high
7.4 0.70 0.00 1.9 0.076 11 34 0.9978 3.51 0.56 9.4 qual_low
7.4 0.66 0.00 1.8 0.075 13 40 0.9978 3.51 0.56 9.4 qual_low 
#figure 2

#corr=cor(exploratory_data_wine, method = "pearson")
corr=cor(wine_df_num, method = "pearson")
ggcorrplot(corr, hc.order = TRUE, 
           lab = TRUE, 
           lab_size = 3, 
           method="square", 
           colors = c("tomato2", "white", "springgreen3"),
           title="Figure 2: Correlation of Variables")

#figure 3.
exploratory_data_wine <- wine_df

attach(exploratory_data_wine)
par(mfrow=c(1,5), oma = c(1,1,1,1) + 0.1,  mar = c(3,3,1,1) + 0.1)

p1 <- ggplot(aes(factor(quality), alcohol), data = exploratory_data_wine) +
  geom_boxplot()  +
  geom_smooth(aes(quality-4,alcohol), method = 'lm',color = 'red') 

p2 <- ggplot(aes(factor(quality), sulphates), data = exploratory_data_wine) +
  geom_boxplot()  +
  geom_smooth(aes(quality-4,sulphates), method = 'lm',color = 'red') 

p3 <- ggplot(aes(factor(quality), pH), data = exploratory_data_wine) +
  geom_boxplot() +
  geom_smooth(aes(quality-4,pH), method = 'lm',color = 'red') 

p4 <- ggplot(aes(factor(quality), density), data = exploratory_data_wine) +
  geom_boxplot()  +
  geom_smooth(aes(quality-4,density), method = 'lm',color = 'red') 

p5 <- ggplot(aes(factor(quality), total_sulfur_dioxide ), data = exploratory_data_wine) +
  geom_boxplot()  +
  geom_smooth(aes(quality-4,total_sulfur_dioxide ), method = 'lm',color = 'red') 

p6 <- ggplot(aes(factor(quality), free_sulfur_dioxide ), data = exploratory_data_wine) +
  geom_boxplot()  +
  geom_smooth(aes(quality-4,free_sulfur_dioxide ), method = 'lm',color = 'red') 

p7 <- ggplot(aes(factor(quality), chlorides), data = exploratory_data_wine) +
  geom_boxplot()  +
  geom_smooth(aes(quality-4,chlorides), method = 'lm',color = 'red') 

p8 <- ggplot(aes(factor(quality), residual_sugar ), data = exploratory_data_wine) +
  geom_boxplot() +
  geom_smooth(aes(quality-4,residual_sugar ), method = 'lm',color = 'red') 

p9 <- ggplot(aes(factor(quality), citric_acid), data = exploratory_data_wine) +
  geom_boxplot() +
  geom_smooth(aes(quality-4,citric_acid), method = 'lm',color = 'red') 

p10 <- ggplot(aes(factor(quality), volatile_acidity), data = exploratory_data_wine) +
  geom_boxplot() +
  geom_smooth(aes(quality-4,volatile_acidity), method = 'lm',color = 'red') 

p11 <- ggplot(aes(factor(quality), fixed_acidity), data = exploratory_data_wine) +
  geom_boxplot() +
  geom_smooth(aes(quality-4,fixed_acidity), method = 'lm',color = 'red') 


detach(exploratory_data_wine)

grid.arrange(p1, p2,p3,p4,p5,p6,p7,p8,p9,p10,p11, nrow = 4, ncol = 3, top = "Figure 3: Box plot to show quality with each variable")
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

## Random Forest Model

fit_control = trainControl(method = "repeatedcv",
                           number = 5,
                           repeats = 3)

set.seed(42)
rf_model = caret::train(quality_cat ~ ., 
                  data = wine_train, 
                  method = "rf", 
                  preProcess = c("scale", "center"),
                  trControl = fit_control,
                  verbose = FALSE)

rf_model
## Random Forest 
## 
## 4549 samples
##   11 predictor
##    2 classes: 'qual_high', 'qual_low' 
## 
## Pre-processing: scaled (11), centered (11) 
## Resampling: Cross-Validated (5 fold, repeated 3 times) 
## Summary of sample sizes: 3639, 3640, 3639, 3639, 3639, 3639, ... 
## Resampling results across tuning parameters:
## 
##   mtry  Accuracy   Kappa    
##    2    0.8125606  0.5871234
##    6    0.8071389  0.5774126
##   11    0.8073591  0.5788010
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.
test_predict = predict(rf_model, wine_test)
confusionMatrix(test_predict, as.factor(wine_test$quality_cat))
## Confusion Matrix and Statistics
## 
##            Reference
## Prediction  qual_high qual_low
##   qual_high      1101      187
##   qual_low        132      528
##                                          
##                Accuracy : 0.8362         
##                  95% CI : (0.819, 0.8524)
##     No Information Rate : 0.633          
##     P-Value [Acc > NIR] : < 2.2e-16      
##                                          
##                   Kappa : 0.6418         
##                                          
##  Mcnemar's Test P-Value : 0.002499       
##                                          
##             Sensitivity : 0.8929         
##             Specificity : 0.7385         
##          Pos Pred Value : 0.8548         
##          Neg Pred Value : 0.8000         
##              Prevalence : 0.6330         
##          Detection Rate : 0.5652         
##    Detection Prevalence : 0.6612         
##       Balanced Accuracy : 0.8157         
##                                          
##        'Positive' Class : qual_high      
##

Feature Importance

Compute the feature importance

rf_model_imp = varImp(rf_model, scale = TRUE)
p1 = rf_model_imp$importance %>%
  as.data.frame() %>%
  rownames_to_column() %>%
  ggplot(aes(x = reorder(rowname, Overall), y = Overall)) +
    geom_bar(stat = "identity", fill = "#1F77B4", alpha = 0.8) +
    coord_flip()

Plot Feature Importance

As per below graph alcohol is most important feature in wine quality.

p1

#### Feature importance breakdown by Category Show overall Feature importance.
Feature importance may differ for low quality and high quality wine. Show Feature importance for both categories.

roc_imp = filterVarImp(x = wine_train[, -ncol(wine_train)], y = wine_train$quality_cat)
p2 = roc_imp %>%
  as.data.frame() %>%
  rownames_to_column() %>%
  ggplot(aes(x = reorder(rowname, qual_high), y = qual_high)) +
    geom_bar(stat = "identity", fill = "#1F77B4", alpha = 0.8) +
    coord_flip()
p3 = roc_imp %>%
  as.data.frame() %>%
  rownames_to_column() %>%
  ggplot(aes(x = reorder(rowname, qual_low), y = qual_high)) +
    geom_bar(stat = "identity", fill = "#1F77B4", alpha = 0.8) +
    coord_flip()
grid.arrange(p1, p2, p3, ncol = 3, widths = c(0.5, 0.5, 0.5))

Iml package - Interpretable Machine Learning

The iml package in R is used for explaining/interpreting machine learning model. It has methods for

  1. Feature importance
  2. Partial dependence plots (Feature Effect)
  3. Individual conditional expectation plots (ICE)
  4. Tree surrogate
  5. LocalModel: Local Interpretable Model-agnostic Explanations (similar to lime)
  6. Shapley value for explaining single predictions

Preparation for explainability

To explain data using Iml,
a) remove the response variable (quality_cat) b) creating a new predictor object that holds the model, the data and the class labels.

X = wine_train %>%
  dplyr::select(-quality_cat) %>%
  as.data.frame()

predictor = Predictor$new(rf_model, data = X, y = wine_train$quality_cat)
str(predictor)
## Classes 'Predictor', 'R6' <Predictor>
##   Public:
##     batch.size: 1000
##     class: NULL
##     clone: function (deep = FALSE) 
##     data: Data, R6
##     initialize: function (model = NULL, data = NULL, predict.function = NULL, 
##     model: train, train.formula
##     predict: function (newdata) 
##     prediction.colnames: NULL
##     prediction.function: function (newdata) 
##     print: function () 
##     task: classification
##   Private:
##     predictionChecked: FALSE

A. Feature Effects and partial dependence plot

Here is function to determine feature importance

feature_imp <- function(my_predictor, my_data, my_feature)  {
  pdp_obj = FeatureEffect$new(my_predictor, feature = my_feature)
  #pdp_obj$center(min(my_data$my_feature))
  glimpse(pdp_obj$results)
  pdp_obj$plot()
}
feature_imp(predictor, wine_train, "alcohol")
## Rows: 42
## Columns: 4
## $ .type   <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale",…
## $ .class  <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qual_low,…
## $ .value  <dbl> -0.006762349, 0.006762349, -0.006762349, 0.006762349, -0.00676…
## $ alcohol <dbl> 8.0, 8.0, 8.9, 8.9, 9.1, 9.1, 9.2, 9.2, 9.4, 9.4, 9.5, 9.5, 9.…

feature_imp(predictor, wine_train, "alcohol")
## Rows: 42
## Columns: 4
## $ .type   <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale",…
## $ .class  <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qual_low,…
## $ .value  <dbl> -0.0043493712, 0.0043493712, -0.0043493712, 0.0043493712, -0.0…
## $ alcohol <dbl> 8.0, 8.0, 8.9, 8.9, 9.1, 9.1, 9.2, 9.2, 9.4, 9.4, 9.5, 9.5, 9.…

feature_imp(predictor, wine_train, "volatile_acidity")
## Rows: 42
## Columns: 4
## $ .type            <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale…
## $ .class           <fct> qual_high, qual_low, qual_high, qual_low, qual_high, …
## $ .value           <dbl> 0.023142238, -0.023142238, 0.023142238, -0.023142238,…
## $ volatile_acidity <dbl> 0.08, 0.08, 0.16, 0.16, 0.18, 0.18, 0.20, 0.20, 0.21,…

feature_imp(predictor, wine_train, "density")
## Rows: 42
## Columns: 4
## $ .type   <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale",…
## $ .class  <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qual_low,…
## $ .value  <dbl> 0.029165590, -0.029165590, 0.033532402, -0.033532402, 0.015833…
## $ density <dbl> 0.98711, 0.98711, 0.98999, 0.98999, 0.99070, 0.99070, 0.99132,…

feature_imp(predictor, wine_train, "total_sulfur_dioxide")
## Rows: 42
## Columns: 4
## $ .type                <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", …
## $ .class               <fct> qual_high, qual_low, qual_high, qual_low, qual_hi…
## $ .value               <dbl> -0.016019079, 0.016019079, -0.016019079, 0.016019…
## $ total_sulfur_dioxide <dbl> 6, 6, 19, 19, 30, 30, 44, 44, 61, 61, 78, 78, 89,…

feature_imp(predictor, wine_train, "sulphates")
## Rows: 42
## Columns: 4
## $ .type     <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale…
## $ .class    <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qual_lo…
## $ .value    <dbl> -0.0043127417, 0.0043127417, -0.0004517378, 0.0004517378, -0…
## $ sulphates <dbl> 0.22, 0.22, 0.35, 0.35, 0.38, 0.38, 0.39, 0.39, 0.41, 0.41, …

feature_imp(predictor, wine_train, "citric_acid")
## Rows: 42
## Columns: 4
## $ .type       <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", "a…
## $ .class      <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qual_…
## $ .value      <dbl> 0.0001099143, -0.0001099143, 0.0001099143, -0.0001099143, …
## $ citric_acid <dbl> 0.00, 0.00, 0.04, 0.04, 0.14, 0.14, 0.20, 0.20, 0.23, 0.23…

feature_imp(predictor, wine_train, "residual_sugar")
## Rows: 42
## Columns: 4
## $ .type          <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale",…
## $ .class         <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qu…
## $ .value         <dbl> -0.0104893047, 0.0104893047, 0.0004396571, -0.000439657…
## $ residual_sugar <dbl> 0.60, 0.60, 1.20, 1.20, 1.30, 1.30, 1.50, 1.50, 1.65, 1…

feature_imp(predictor, wine_train, "pH")
## Rows: 42
## Columns: 4
## $ .type  <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", …
## $ .class <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qual_low, …
## $ .value <dbl> 0.001836462, -0.001836462, 0.001836462, -0.001836462, 0.0018364…
## $ pH     <dbl> 2.74, 2.74, 2.97, 2.97, 3.02, 3.02, 3.06, 3.06, 3.08, 3.08, 3.1…

feature_imp(predictor, wine_train, "fixed_acidity")
## Rows: 42
## Columns: 4
## $ .type         <chr> "ale", "ale", "ale", "ale", "ale", "ale", "ale", "ale", …
## $ .class        <fct> qual_high, qual_low, qual_high, qual_low, qual_high, qua…
## $ .value        <dbl> 0.002523003, -0.002523003, 0.002523003, -0.002523003, 0.…
## $ fixed_acidity <dbl> 3.8, 3.8, 5.7, 5.7, 6.0, 6.0, 6.1, 6.1, 6.3, 6.3, 6.4, 6…

two_feature_pdp = FeatureEffects$new(predictor, feature = c("sulphates",  "pH" ))
two_feature_pdp$plot()

multiple_feature_pdp = FeatureEffects$new(predictor, feature = c("volatile_acidity", 
                "density", "total_sulfur_dioxide", "sulphates", "citric_acid", 
                "residual_sugar", "pH", "fixed_acidity" ))
multiple_feature_pdp$plot()

Feature Interaction

Show Alcohol and other Features

interact = Interaction$new(predictor, feature = "alcohol")

#plot(interact)
interact$results %>%
  ggplot(aes(x = reorder(.feature, .interaction), y = .interaction, fill = .class)) +
    facet_wrap(~ .class, ncol = 2) +
    geom_bar(stat = "identity", alpha = 0.8) +
    scale_fill_tableau() +
    coord_flip() +
    guides(fill = FALSE)
## Warning: `guides(<scale> = FALSE)` is deprecated. Please use `guides(<scale> =
## "none")` instead.

interact_pH = Interaction$new(predictor, feature = "pH")
#plot(interact)
interact_pH$results %>%
  ggplot(aes(x = reorder(.feature, .interaction), y = .interaction, fill = .class)) +
    facet_wrap(~ .class, ncol = 2) +
    geom_point(stat = "identity", alpha = 0.8) +
    scale_fill_tableau() +
    coord_flip() +
    guides(fill = "none")

tree = TreeSurrogate$new(predictor, maxdepth = 5)
tree$r.squared
## [1] 0.2850662 0.2850662
glimpse(tree)
## Classes 'TreeSurrogate', 'InterpretationMethod', 'R6' <TreeSurrogate>
##   Inherits from: <InterpretationMethod>
##   Public:
##     clone: function (deep = FALSE) 
##     initialize: function (predictor, maxdepth = 2, tree.args = NULL) 
##     maxdepth: 5
##     plot: function (...) 
##     predict: function (newdata, type = "prob", ...) 
##     predictor: Predictor, R6
##     print: function () 
##     r.squared: 0.285066197773788 0.285066197773788
##     results: data.frame
##     tree: constparty, party
##   Private:
##     aggregate: function () 
##     compute_r2: function (predict.tree, predict.model) 
##     dataDesign: data.table, data.frame
##     dataSample: data.table, data.frame
##     feature.names: NULL
##     finished: TRUE
##     flush: function () 
##     generatePlot: function () 
##     getData: function (...) 
##     intervene: function () 
##     match_cols: function (newdata) 
##     multiClass: TRUE
##     object.predict.colnames: .y.hat.qual_high .y.hat.qual_low
##     plotData: NULL
##     predictResults: data.frame
##     printParameters: function () 
##     q: function (x) 
##     qResults: data.frame
##     run: function (force = FALSE, ...) 
##     run.prediction: function (dataDesign) 
##     sampler: Data, R6
##     tree.args: NULL
##     tree.predict.colnames: .y.hat.tree.qual_high .y.hat.tree.qual_low
##     weightSamples: function ()
plot(tree)

#glimpse(prediction)
#It will be huge list
#print(tree$results)
#tree

Local Model: (LIME Type) model explainability

LocalModel is Lime implementation in R. It enable you to bold explain local/individual prediction bold To explain a row i from text set use below method

lime_imp <- function(my_predictor, my_data, row_num)  {
  lime_explain <- LocalModel$new(my_predictor, x.interest = my_data[row_num, ])
  return (lime_explain)
}
# remove categorical column 12
#Get lime explaination for row 10 and 20
i <- 10
j <- 20
my_lime_explaination_1 = lime_imp(predictor,wine_test[,-12],i)
## Loading required package: glmnet
## Loaded glmnet 4.1-3
my_lime_explaination_2 = lime_imp(predictor,wine_test[,-12],j)
my_lime_explaination_1$results

Above example show prediction and impact

plot(my_lime_explaination_1)

p1 = my_lime_explaination_1$results %>%
  ggplot(aes(x = reorder(feature.value, -effect), y = effect, fill = .class)) +
    facet_wrap(~ .class, ncol = 2) +
    geom_bar(stat = "identity", alpha = 0.8) +
    scale_fill_tableau() +
    coord_flip() +
    labs(title = paste0("Local Model (LIME) Test case #", i)) +
    guides(fill = "none")
p1

p2 = my_lime_explaination_2$results %>%
  ggplot(aes(x = reorder(feature.value, -effect), y = effect, fill = .class)) +
    facet_wrap(~ .class, ncol = 2) +
    geom_bar(stat = "identity", alpha = 0.8) +
    scale_fill_tableau() +
    coord_flip() +
    labs(title = paste0("Local Model (LIME) Test case #", j)) +
    guides(fill = "none")
p2

grid.arrange(p1, p2, ncol = 2)

Shapley

Below method show how to use Shapley value for explaining prediction. It compute feature prediction with Shapley value for cooperative game theory.

my_shapley = Shapley$new(predictor, x.interest = wine_test[,-12][i, ])
head(my_shapley$results)
my_shapley$results %>%
  ggplot(aes(x = reorder(feature.value, -phi), y = phi, fill = class)) +
    facet_wrap(~ class, ncol = 2) +
    geom_bar(stat = "identity", alpha = 0.8) +
    scale_fill_tableau() +
    coord_flip() +
    guides(fill = "none")

Above plot shows result from Shapley - difference of instance prediction and dataset average predition among the features.

DALEX

DALEX: Descriptive machine Learning EXplanations Dalex package contains various explianers that identify relationship between dependent and independant variables.

pred_fun = function(object, newdata){predict(object, newdata = newdata, type = "prob")[, 2]}
yTest_data = as.numeric(wine_test$quality_cat)

explainer_classif_rf = DALEX::explain(rf_model, label = "rf",
                                       data = wine_test, y = yTest_data,
                                       predict_function = pred_fun)
## Preparation of a new explainer is initiated
##   -> model label       :  rf 
##   -> data              :  1948  rows  12  cols 
##   -> data              :  tibble converted into a data.frame 
##   -> target variable   :  1948  values 
##   -> predict function  :  pred_fun 
##   -> predicted values  :  No value for predict function target column. (  default  )
##   -> model_info        :  package caret , ver. 6.0.90 , task classification (  default  ) 
##   -> predicted values  :  numerical, min =  0 , mean =  0.3728943 , max =  0.982  
##   -> residual function :  difference between y and yhat (  default  )
##   -> residuals         :  numerical, min =  0.09 , mean =  0.9941489 , max =  1.958  
##   A new explainer has been created! 

Feature Importance

variable_importance function have feature importance.

my_classifier = model_performance(explainer_classif_rf)
my_classifier_variable_importance <- variable_importance(explainer_classif_rf, loss_function = loss_root_mean_square)
plot(my_classifier_variable_importance )

Variable Response

Use variable_response function to compute marginal response

vr_alcohol_response  = model_profile(explainer_classif_rf, variable = "alcohol", type = "partial")
vr_density_response  = model_profile(explainer_classif_rf, variable = "density", type = "partial")
vr_fixed_response  = model_profile(explainer_classif_rf, variable = "fixed_acidity", type = "partial")
vr_volatile_response  = model_profile(explainer_classif_rf, variable = "volatile_acidity", type = "partial")

vr_citric_response  = model_profile(explainer_classif_rf, variable = "citric_acid", type = "partial")
vr_chlorides_response  = model_profile(explainer_classif_rf, variable = "chlorides", type = "partial")
vr_free_response  = model_profile(explainer_classif_rf, variable = "free_sulfur_dioxide", type = "partial")
vr_total_response  = model_profile(explainer_classif_rf, variable = "total_sulfur_dioxide", type = "partial")

vr_pH  = model_profile(explainer_classif_rf, variable = "pH", type = "partial")
vr_sulphates  = model_profile(explainer_classif_rf, variable = "sulphates", type = "partial")
plot(vr_alcohol_response)

plot(vr_density_response)

plot(vr_fixed_response)

plot(vr_volatile_response)

plot(vr_citric_response )

plot(vr_chlorides_response)

plot(vr_free_response)

plot(vr_total_response)

plot(vr_pH)

plot(vr_sulphates )

ale_alcohol_response   = model_profile(explainer_classif_rf, variable = "alcohol", type = "accumulated")

ale_density_response  = model_profile(explainer_classif_rf, variable = "density", type = "accumulated")
ale_fixed_response  = model_profile(explainer_classif_rf, variable = "fixed_acidity", type = "accumulated")
ale_volatile_response  = model_profile(explainer_classif_rf, variable = "volatile_acidity", type = "accumulated")

ale_citric_response  = model_profile(explainer_classif_rf, variable = "citric_acid", type = "accumulated")
ale_chlorides_response  = model_profile(explainer_classif_rf, variable = "chlorides", type = "accumulated")
ale_free_response  = model_profile(explainer_classif_rf, variable = "free_sulfur_dioxide", type = "accumulated")
ale_total_response  = model_profile(explainer_classif_rf, variable = "total_sulfur_dioxide", type = "accumulated")

ale_pH  = model_profile(explainer_classif_rf, variable = "pH", type = "partial")
ale_sulphates  = model_profile(explainer_classif_rf, variable = "sulphates", type = "partial")


plot(ale_alcohol_response)

plot(ale_density_response)

plot(ale_fixed_response)

plot(ale_volatile_response)

plot(ale_citric_response )

plot(ale_chlorides_response)

plot(ale_free_response)

plot(ale_total_response)

plot(ale_pH)

plot(ale_sulphates )

Breakdown

Breakdown is model agnostic tool for decomposition of predictions

X2 = wine_test[,-12]
predict.function = function(model, new_observation) {
  predict(model, new_observation, type="prob")[,2]
}
predict.function(rf_model, wine_test[,-12][1, ])
## [1] 0.94
br = broken(model = rf_model, 
             new_observation = X2[1, ], 
             data = X, 
             baseline = "Intercept", 
             predict.function = predict.function, 
             keep_distributions = TRUE)
br
##                             contribution
## (Intercept)                        0.000
## + volatile_acidity = 0.7           0.143
## + alcohol = 9.4                    0.143
## + density = 0.9978                 0.044
## + chlorides = 0.076                0.056
## + fixed_acidity = 7.4              0.020
## + residual_sugar = 1.9             0.019
## + sulphates = 0.56                 0.013
## + citric_acid = 0                  0.004
## + pH = 3.51                        0.035
## + free_sulfur_dioxide = 11         0.044
## + total_sulfur_dioxide = 34        0.052
## final_prognosis                    0.573
## baseline:  0.3667738
item_to_explain<- wine_test[,-12][1, ]
my_breakdown.function = function(my_model, item_to_exlain) {
  predict(my_model, item_to_explain, type="prob")[,2]
}
my_breakdown.function(rf_model, item_to_explain)
## [1] 0.94
my_breakdown = broken(model = rf_model, 
             new_observation = item_to_explain, 
             data = X, 
             baseline = "Intercept", 
             predict.function = predict.function, 
             keep_distributions = TRUE)
my_breakdown
##                             contribution
## (Intercept)                        0.000
## + volatile_acidity = 0.7           0.143
## + alcohol = 9.4                    0.143
## + density = 0.9978                 0.044
## + chlorides = 0.076                0.056
## + fixed_acidity = 7.4              0.020
## + residual_sugar = 1.9             0.019
## + sulphates = 0.56                 0.013
## + citric_acid = 0                  0.004
## + pH = 3.51                        0.035
## + free_sulfur_dioxide = 11         0.044
## + total_sulfur_dioxide = 34        0.052
## final_prognosis                    0.573
## baseline:  0.3667738
data.frame(y = my_breakdown$contribution,
           x = my_breakdown$variable) %>%
  ggplot(aes(x = reorder(x, y), y = y)) +
    geom_bar(stat = "identity", fill = "#1F77B4", alpha = 0.8) +
    coord_flip()

plot(my_breakdown)

plot(my_breakdown, plot_distributions = TRUE) + ggtitle ("Breakdown plot") 
## Warning: `fun.y` is deprecated. Use `fun` instead.



Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next