Model awal

Anda menerima sebuah himpunan data berisi pengukuran gaya gravitasi antara dua benda pada berbagai jarak, dan ditantang untuk membangun model sederhana yang memprediksi gaya tersebut untuk jarak tertentu. Awalnya, Anda ingin menggunakan regresi linear sederhana. Data terdiri dari 120 pasang distance dan force, dan telah dimuat untuk Anda sebagai newton.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Bangun model linear untuk data newton menggunakan fungsi model linear dari R dasar dan simpan sebagai lr_force.
Buat data frame baru df dengan menggabungkan nilai prediksi ke data newton asli.
Hasilkan scatterplot force terhadap distance menggunakan ggplot().
Tambahkan garis regresi ke scatterplot dengan nilai hasil fitting.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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 dan Jalankan Kode

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

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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!