Data

To illustrate application of auditor we will use dataset “dragons” available in the DALEX package. The dataset contains characteristics of fictional creatures (dragons), like year of birth, height, weight, etc (see below). The goal is to predict the length of life of dragons (a regression problem).

library(DALEX)
data(dragons)
head(dragons)
##   year_of_birth   height   weight scars colour year_of_discovery
## 1         -1291 59.40365 15.32391     7    red              1700
## 2          1589 46.21374 11.80819     5    red              1700
## 3          1528 49.17233 13.34482     6    red              1700
## 4          1645 48.29177 13.27427     5  green              1700
## 5            -8 49.99679 13.08757     1    red              1700
## 6           915 45.40876 11.48717     2    red              1700
##   number_of_lost_teeth life_length
## 1                   25   1368.4331
## 2                   28   1377.0474
## 3                   38   1603.9632
## 4                   33   1434.4222
## 5                   18    985.4905
## 6                   20    969.5682

Models

First, we need models to compare. We selected linear regression and random forest because of their different structures. Linear regression model linear relationships between target response and independent variables. While random forest should be able to capture also non-linear relationships between variables.

# Linear regression
lm_model <- lm(life_length ~ ., data = dragons)

# Random forest
library(randomForest)
set.seed(59)
rf_model <- randomForest(life_length ~ ., data = dragons)

Preparation for analysis of performance

Analysis begins with creation of an explainer object with explain function from DALEX package. Explainer wraps a model with its meta-data, such as dataset that was used for training or observed response.

lm_exp <- DALEX::explain(lm_model, label = "lm", data = dragons, y = dragons$life_length)
rf_exp <- DALEX::explain(rf_model, label = "rf", data = dragons, y = dragons$life_length)

Next step requires creation of model_performance objects of each explained model. The function computes chosen performance measures for passed model.

library(auditor)
lm_mp <- model_performance(lm_exp)
rf_mp <- model_performance(rf_exp)

lm_mp
##           0%          10%          20%          30%          40% 
## -113.8603163  -52.8746073  -36.0829295  -22.1577386  -10.5740342 
##          50%          60%          70%          80%          90% 
##   -0.3623974   11.3530576   24.7083253   37.8880242   54.0588747 
##         100% 
##  108.2061554

Model ranking radar plot

Model performance measures may be plotted together to easily compare model performances. A result further from the center of the plot means a better model performance. Parameter verbose indicates whether the table with scores should be generated. On the plot scores are inversed and scaled to [0,1].

plot(lm_mp, rf_mp)

plot of chunk unnamed-chunk-5

# alternative:
# plot_radar(lm_mp, rf_mp, verbose = FALSE)

There is a possibiliy to define functions with custom model performance measure.

new_score <- function(object) sum(sqrt(abs(object$residuals)))

lm_mp <- model_performance(lm_exp,  
                          score = c("mae", "mse", "rec", "rroc"), 
                          new_score = new_score)

rf_mp <- model_performance(rf_exp,  
                          score = c("mae", "mse", "rec", "rroc"), 
                          new_score = new_score)

plot(lm_mp, rf_mp)

plot of chunk unnamed-chunk-6

Other methods

Other methods and plots are described in vignettes: