Creare manualmente una feature

Dopo alcune ricerche con il tuo team, ricordi che la forza gravitazionale di attrazione tra due corpi obbedisce alla formula di Newton:

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

Non puoi usare direttamente la formula perché le masse sono sconosciute, ma puoi adattare un modello di regressione di force in funzione di inv_square_distance. L’insieme di dati aumentato df che hai costruito nell’esercizio precedente è stato caricato per te.

Questo esercizio fa parte del corso

Feature Engineering in R

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Istruzioni dell'esercizio

Crea una nuova variabile inv_square_distance, definita come il reciproco della distanza al quadrato, e aggiungila al data frame df.
Costruisci un semplice modello di regressione con lm() di force versus inv_square_distance e salvalo come lr_force_2.
Associa le tue predizioni a df_inverse.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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()

Modifica ed esegui il codice

Feature Engineering in R

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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!