Binding models together

When running several models, it is useful to summarize your results for comparison, both as a tibble and as a parallel coordinates chart. The glm_metrics object that your created is loaded in the workspace, as well as the corresponding bayes_metrics and mixed_metrics. The GGally package is ready for you.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Bind all three models by rows.
Create a parallel coordinates plot to compare the models' performance in both metrics.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

model <- c("glm", "glm","bayes","bayes", "mixed", "mixed")

# Bind models by rows
models <- ___(glm_metrics,bayes_metrics, mixed_metrics)%>%
  add_column(model = model)%>%
  select(-.estimator) %>%
  spread(model,.estimate)

models

# Create a parallel coordinates plot
___(models,
           columns = 2:4, groupColumn = 1,
           scale="globalminmax",
           showPoints = TRUE)

Edit and Run Code

Feature Engineering in R

IntermediateSkill Level

4.9+

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You’ll wrap up the course by learning about feature engineering and machine learning techniques. You’ll begin by focusing on the problems associated with using all available features in a model and the importance of identifying irrelevant and redundant features and learning to remove these features using embedded methods such as lasso and elastic-net. Next, you’ll explore shrinkage methods such as lasso, ridge, and elastic-net, which can be used to regularize feature weights or select features by setting coefficients to zero. Finally, you’ll finish by focusing on creating an end-to-end feature engineering workflow and reviewing and practicing the previously learned concepts and functions in a small project.

Exercise 1: Reducing the model's features Exercise 2: Sifting through variable importance Exercise 3: Assessing model performance using all available predictors Exercise 4: Building a reduced model Exercise 5: Shrinkage methods Exercise 6: Manual regularization with Lasso Exercise 7: Tuning the penalty Exercise 8: Finalizing the model Exercise 9: Putting it all together Exercise 10: Prep and split Exercise 11: Preprocess Exercise 12: Model Exercise 13: Assess Exercise 14: Congratulations!