A tentative model

You are handed a data set with measures of the gravitational force between two bodies at different distances and are challenged to build a simple model to predict such force given a specific distance. Initially, you want to stick to simple linear regression. The data consist of 120 pairs of distance and force, and is loaded for you as newton.

This exercise is part of the course

Feature Engineering in R

View Course

Exercise instructions

Build a linear model for the newton data using the linear model from base R function and assign it to lr_force.
Create a new data frame df by binding the prediction values to the original newton data.
Generate a scatterplot of force versus distance using ggplot().
Add a regression line to the scatterplot with the fitted values.

Hands-on interactive exercise

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

# Build a linear model for the newton the data and assign it to lr_force
lr_force <- ___(force ~ distance, data = ___)

# Create a new data frame by binding the prediction values to the original data
df <- newton %>% ___(lr_pred = predict(lr_force))

# Generate a scatterplot of force vs. distance
df %>%
  ggplot(aes(x = distance, y = force)) +
  geom____() +
# Add a regression line with the fitted values
  geom_line(aes(y = ___), color = "blue", lwd = .75) +
  ggtitle("Linear regression of force vs. distance") +
  theme_classic()

Edit and Run Code

Feature Engineering in R

IntermediateSkill Level

4.8+

93 reviews

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!