Manually engineering a feature

After doing some research with your team, you recall that the gravitational force of attraction between two bodies obeys Newton's formula:

$$F = G\frac{m_1m_2}{r^2}$$.

You can't use the formula directly because the masses are unknown, but you can fit a regression model of force as a function of inv_square_distance. The augmented dataset df you built in the previous exercise has been loaded for you.

This exercise is part of the course

Feature Engineering in R

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Exercise instructions

Create a new variable inv_square_distance defined as the reciprocal of the squared distance and add it to the df data frame.
Build a simple regression model using lm() of force versus inv_square_distance and save it as lr_force_2.
Bind your predictions to df_inverse.

Hands-on interactive exercise

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

# Create a new variable inv_square_distance
df_inverse <- df %>% ___(inv_square_distance = 1/distance^2)

# Build a simple regression model
lr_force_2 <- lm(force ~ ___, data = df_inverse)

# Bind your predictions to df_inverse
df_inverse <- df_inverse %>% ___(lr2_pred = predict(lr_force_2))

df_inverse %>% ggplot(aes(x = distance, y = force)) +
  geom_point() +
  geom_line(aes(y = lr2_pred), col = "blue", lwd = .75) +
  ggtitle("Linear regression of force vs. inv_square_distance") +
  theme_classic()

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Feature Engineering in R

IntermediateSkill Level

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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!