# NOT RUN {
library("breakDown")
new.wine <- data.frame(citric.acid = 0.35,
sulphates = 0.6,
alcohol = 12.5,
pH = 3.36,
residual.sugar = 4.8)
wine_lm_model4 <- lm(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine)
wine_lm_explainer4 <- explain(wine_lm_model4, data = wine, label = "model_4v")
wine_lm_predict4 <- prediction_breakdown(wine_lm_explainer4, observation = new.wine)
wine_lm_predict4
plot(wine_lm_predict4)
# }
# NOT RUN {
library("randomForest")
wine_rf_model4 <- randomForest(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine)
wine_rf_explainer4 <- explain(wine_rf_model4, data = wine, label = "model_rf")
wine_rf_predict4 <- prediction_breakdown(wine_rf_explainer4, observation = new.wine)
wine_rf_predict4
plot(wine_rf_predict4)
library("gbm")
# create a gbm model
model <- gbm(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine,
distribution = "gaussian",
n.trees = 1000,
interaction.depth = 4,
shrinkage = 0.01,
n.minobsinnode = 10,
verbose = FALSE)
# make an explainer for the model
explainer_gbm <- explain(model, data = wine, predict_function =
function(model, x) predict(model, x, n.trees = 1000))
# create a new observation
exp_sgn <- prediction_breakdown(explainer_gbm, observation = new.wine)
exp_sgn
plot(exp_sgn)
# }
# NOT RUN {
# }
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