DALEX has a new skin!
Learn how it was designed at gdansk2019.satRdays March 12, 2019 By smarterpoland
DALEX (Descriptive mAchine Learning EXplanations)
DALEX is an R package for visual explanation, exploration, diagnostic and debugging of predictive ML models
Recently Hanna Dyrcz designed a new beautiful theme for these explainers. Hanna is a very talented designer.
devtools::install_github("pbiecek/DALEX")
library(knitr); library(dplyr)
library(randomForest)
library(e1071)
library(rms)
library(gbm)
library(caret); library(data.table)
library("DALEX")
titanic <- na.omit(titanic)
titanic %>% head %>% kable()
| gender | age | class | embarked | country | fare | sibsp | parch | survived |
|---|---|---|---|---|---|---|---|---|
| male | 42 | 3rd | Southampton | United States | 7.11 | 0 | 0 | no |
| male | 13 | 3rd | Southampton | United States | 20.05 | 0 | 2 | no |
| male | 16 | 3rd | Southampton | United States | 20.05 | 1 | 1 | no |
| female | 39 | 3rd | Southampton | England | 20.05 | 1 | 1 | yes |
| female | 16 | 3rd | Southampton | Norway | 7.13 | 0 | 0 | yes |
| male | 25 | 3rd | Southampton | United States | 7.13 | 0 | 0 | yes |
모델 준비
model_titanic_rf <- randomForest(as.factor(survived == "yes") ~ gender + age + class + embarked +
fare + sibsp + parch, data = titanic)
explain_titanic_rf <- explain(model_titanic_rf,
data = titanic[,-9],
y = titanic$survived == "yes",
label = "Random Forest v7")
vi_rf <- variable_importance(explain_titanic_rf)
plot(vi_rf)
기존의 variable importance plot과의 비교
library(caret)
varImpPlot(model_titanic_rf,type=2)
변수의 효과
Gender가 가장 중요한 변수
class, Age , Fare 가 그 다음 중요한 세가지 변수
모델의 반응과 이 변수들 사이의 관계를 보고자 한다.
“univariate relation” : can be calculated with variable_response()
Passanger class
Passangers in the first class have much higher survival probability.
vr_class <- variable_response(explain_titanic_rf, variable = "class", type="factor")
plot(vr_class)
## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.
Age
5살 이하의 어린이들이 살 확률이 더 높았다.
vr_age <- variable_response(explain_titanic_rf, variable = "age")
plot(vr_age, use_facets = TRUE)
Fare
가장 저렴한 티켓은 낮은 생존 확률과 연관되어 있다.
vr_fare <- variable_response(explain_titanic_rf, variable = "fare")
plot(vr_fare, use_facets = TRUE)
Embarked
Cherbourg에서의 탑승객들이 가장 높은 생존을 보인다.
vr_embarked <- variable_response(explain_titanic_rf, variable = "embarked", type="factor")
plot(vr_embarked)
## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.
Instance level explanations
Let’s see break down explanation for model predictions for 8 years old male from 1st class that embarked from port C.
new_passanger <- data.frame(
class = factor("1st", levels = c("1st", "2nd", "3rd", "deck crew", "engineering crew", "restaurant staff", "victualling crew")),
gender = factor("male", levels = c("female", "male")),
age = 8,
sibsp = 0,
parch = 0,
fare = 72,
embarked = factor("Southampton", levels = c("Belfast", "Cherbourg", "Queenstown", "Southampton"))
)
new_passanger %>% kable()
| class | gender | age | sibsp | parch | fare | embarked |
|---|---|---|---|---|---|---|
| 1st | male | 8 | 0 | 0 | 72 | Southampton |
sp_rf <- single_prediction(explain_titanic_rf, new_passanger)
plot(sp_rf)
이 탑승객에게 가장 중요한 변수는 나이와 성별로 보인다. 전반적으로 이 탑승객의 생존 odds는 평균적인 탑승객보다 높음을 알 수 있다. 주된 요인은 남자임에도 어린 나이 때문인 것으로 생각된다.
다양한 모델들
Logistic regression
model_titanic_lmr <- lrm(survived == "yes" ~ class + gender + rcs(age) + sibsp +
parch + fare + embarked, titanic)
explain_titanic_lmr <- explain(model_titanic_lmr, data = titanic,
y = titanic$survived == "yes",
predict_function = function(m,x) predict(m, x, type="fitted"),
label = "Logistic regression")
Generalized Boosted Models (GBM)
model_titanic_gbm <- gbm(survived == "yes" ~ class + gender + age + sibsp +
parch + fare + embarked, data = titanic, n.trees = 15000)
## Distribution not specified, assuming bernoulli ...
explain_titanic_gbm <- explain(model_titanic_gbm, data = titanic,
y = titanic$survived == "yes",
predict_function = function(m,x) predict(m, x, n.trees = 15000, type = "response"),
label = "Generalized Boosted Models")
Support Vector Machines (SVM)
model_titanic_svm <- svm(survived == "yes" ~ class + gender + age + sibsp +
parch + fare + embarked, data = titanic,
type = "C-classification", probability = TRUE)
explain_titanic_svm <- explain(model_titanic_svm, data = titanic,
y = titanic$survived == "yes",
label = "Support Vector Machines")
k-Nearest Neighbours (kNN)
model_titanic_knn <- knn3(survived == "yes" ~ class + gender + age + sibsp +
parch + fare + embarked, data = titanic, k = 5)
explain_titanic_knn <- explain(model_titanic_knn, data = titanic,
y = titanic$survived == "yes",
predict_function = function(m,x) predict(m, x)[,2],
label = "k-Nearest Neighbours")
Variable performance
vi_rf <- variable_importance(explain_titanic_rf)
vi_lmr <- variable_importance(explain_titanic_lmr)
vi_gbm <- variable_importance(explain_titanic_gbm)
vi_svm <- variable_importance(explain_titanic_svm)
vi_knn <- variable_importance(explain_titanic_knn)
plot(vi_rf, vi_lmr, vi_gbm, vi_svm, vi_knn, bar_width = 4)
Single variable
vr_age_rf <- variable_response(explain_titanic_rf, variable = "age")
vr_age_lmr <- variable_response(explain_titanic_lmr, variable = "age")
vr_age_gbm <- variable_response(explain_titanic_gbm, variable = "age")
vr_age_svm <- variable_response(explain_titanic_svm, variable = "age")
vr_age_knn <- variable_response(explain_titanic_knn, variable = "age")
plot(vr_age_rf, vr_age_lmr, vr_age_gbm, vr_age_svm, vr_age_knn)
plot(vr_age_rf, vr_age_lmr, vr_age_gbm, vr_age_svm, vr_age_knn, use_facets = TRUE)
Instance level explanations
sp_rf <- single_prediction(explain_titanic_rf, new_passanger)
sp_lmr <- single_prediction(explain_titanic_lmr, new_passanger)
sp_gbm <- single_prediction(explain_titanic_gbm, new_passanger)
sp_svm <- single_prediction(explain_titanic_svm, new_passanger)
sp_knn <- single_prediction(explain_titanic_knn, new_passanger)
plot(sp_rf, sp_lmr, sp_gbm, sp_svm, sp_knn)
