Machine Learning Essentials: Practical Guide in R by Kassambara Alboukadel

Author:Kassambara, Alboukadel [Kassambara, Alboukadel]
Language: eng
Format: epub
Published: 2018-03-10T00:00:00+00:00

# Build the model set.seed (123 ) elastic <- train ( medv ~ ., data = train.data, method = "glmnet" , trControl = trainControl ("cv" , number = 10 ), tuneLength = 10 ) # Model coefficients coef (elastic$ finalModel, elastic$ bestTune$ lambda) # Make predictions predictions <- elastic %>% predict (test.data) # Model prediction performance data.frame ( RMSE = RMSE (predictions, test.data$ medv), Rsquare = R2 (predictions, test.data$ medv) )

Comparing models performance :

The performance of the different models - ridge, lasso and elastic net - can be easily compared using caret . The best model is defined as the one that minimizes the prediction error.

models <- list (ridge = ridge, lasso = lasso, elastic = elastic) resamples (models) %>% summary ( metric = "RMSE" )

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