Memvisualisasikan ragam yang dijelaskan

Setelah semua perhitungan selesai, akan sangat membantu jika data direpresentasikan secara visual. Sekarang Anda akan membuat plot kolom yang menampilkan ragam yang dijelaskan oleh setiap komponen utama.

Vektor variable_explained yang Anda buat pada latihan sebelumnya tersedia, dan ggplot() theme_() telah disetel ke classic.

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

Lihat Kursus

Petunjuk latihan

Gunakan informasi dalam PCA tibble untuk membuat plot kolom dari ragam yang dijelaskan.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

PCA = tibble(PC = 1:length(sdev), var_explained = var_explained, 
       cumulative = cumsum(var_explained))

# Use the information in the PCA tibble to create a column plot of variance explained
PCA %>% ggplot(aes(x = ___, y = ___)) +
  geom_col(fill = "steelblue") +
  xlab("Principal components") +
  ylab("Variance explained")

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Rekayasa Fitur di R

SkillTag.level.intermediateSkillTag.label

4.8+

120 reviews

Mulai Kursus Gratis

Raw data does not always come in its best shape for analysis. In this opening chapter, you will get a first look at how to transform and create features that enhance your model's performance and interpretability.

Exercise 1: What is feature engineering?Exercise 2: A tentative model Exercise 3: Manually engineering a feature Exercise 4: Creating new features using domain knowledge Exercise 5: Setting up your data for analysis Exercise 6: Building a workflow Exercise 7: Increasing the information content of raw data Exercise 8: Identifying missing values Exercise 9: Imputing missing values and creating dummy variables Exercise 10: Fitting and assessing the model Exercise 11: Predicting hotel bookings

In this chapter, you’ll learn that, beyond manually transforming features, you can leverage tools from the tidyverse to engineer new variables programmatically. You’ll explore how this approach improves your models' reproducibility and is especially useful when handling datasets with many features.

Exercise 1: Why transform existing features?Exercise 2: Glancing at your data Exercise 3: Normalizing and log-transforming Exercise 4: Fit and augment Exercise 5: Customize your model assessment Exercise 6: Common feature transformations Exercise 7: Common transformations Exercise 8: Plain recipe Exercise 9: Box-Cox transformation Exercise 10: Yeo-Johnson transformation Exercise 11: Advanced transformations Exercise 12: Baseline Exercise 13: step_poly()Exercise 14: step_percentile()Exercise 15: Who's staying?

You’ll now learn how models often benefit from reducing dimensionality and extracting features from high-dimensional data, including converting text data into numeric values, encoding categorical data, and ranking the predictive power of variables. You’ll explore methods including principal component analysis, kernel principal component analysis, numerical extraction from text, categorical encodings, and variable importance scores.

Exercise 1: Mengurangi Dimensionalitas Exercise 2: Menyiapkan panggung Exercise 3: Menyelami struktur Exercise 4: Persentase varians yang dijelaskan Exercise 5: Memvisualisasikan ragam yang dijelaskan

Latihan Saat Ini

Exercise 6: Feature hashing Exercise 7: Menyelidiki bidang pendidikan Exercise 8: Masuk ke matriks Exercise 9: Mengeksplorasi hashing Exercise 10: Memvisualisasikan hashing Exercise 11: Mengodekan data kategorikal menggunakan pembelajaran terawasi Exercise 12: Menyiapkan workflow Anda Exercise 13: Membangun, mengaugmentasi, dan menilai Exercise 14: Menggabungkan model menjadi satu Exercise 15: Kepentingan Variabel Exercise 16: Buat sebuah workflow Exercise 17: Fit dan augment Exercise 18: Prediktor utama yang mana?

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!