05-exercises.Rmd

# Exercises

Examples in previous chapters were based on random forest model and linear model.
But there are so many different approaches for regression modeling.
Try `gbm` (Generalized Boosted Regression Models), `knn` (k-Nearest Neighbour), `svm` (Support Vector Machines), `nnet` (Neural Networks) or other models.

1. Prepare explainers for model performance,
2. Prepare explainers for variable importance,
3. Prepare explainers for variable response for `construction.year`,
4. Prepare explainers for model predictions.

Below you will find fits for few different models. Try them out.


```{r Exercises, message=FALSE, warning=FALSE, fig.height=4, fig.cap="Distribution of residuals for fourn new models"}
library("DALEX")
library("gbm")

apartments_gbm_model <- gbm(m2.price ~ construction.year + surface + floor + 
                         no.rooms + district, data = apartments, n.trees = 1000)

explainer_gbm <- explain(apartments_gbm_model, 
                          data = apartmentsTest[,2:6], y = apartmentsTest$m2.price,
                         predict_function = function(m, d) predict(m, d, n.trees = 1000))

library("nnet")
apartments_nnet_model <- nnet(m2.price ~ construction.year + surface + floor + 
                         no.rooms + district, data = apartments,
                         linout=TRUE,
                         size = 50, maxit=100)

explainer_nnet <- explain(apartments_nnet_model, 
                          data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)

library("e1071")
apartments_svm_model <- svm(m2.price ~ construction.year + surface + floor + 
                         no.rooms + district, data = apartments)

explainer_svm <- explain(apartments_svm_model, 
                          data = apartmentsTest[,2:6], y = apartmentsTest$m2.price)

library("caret")
mapartments <- model.matrix(m2.price ~ ., data = apartments)
mapartmentsTest <- model.matrix(m2.price ~ ., data = apartmentsTest)
apartments_knn_model <- knnreg(mapartments, apartments[,1], k = 5)

explainer_knn <- explain(apartments_knn_model, 
                          data = mapartmentsTest, y = apartmentsTest$m2.price)

# Model performance

mp_knn <- model_performance(explainer_knn)
mp_svm <- model_performance(explainer_svm)
mp_gbm <- model_performance(explainer_gbm)
mp_nnet <- model_performance(explainer_nnet)
plot(mp_gbm, mp_nnet, mp_svm, mp_knn, geom = "boxplot")
```