Membuat fitur baru

Feature engineering juga mencakup pembuatan fitur baru. Membuat fitur baru penting karena model bergantung pada fitur tersebut untuk akurasi prediksi. Pada latihan ini, Anda akan memeriksa properti tiga kolom yang berupa bilangan bulat dalam data tetapi sebenarnya merepresentasikan nilai kategorikal. Ketiga kolom tersebut adalah: search_engine_type, product_type, dan advertiser_type. Anda akan membuat fitur hitung untuk 3 kolom tersebut, serta untuk device_id dan site_id. Fitur hitung ini merepresentasikan jumlah klik untuk masing-masing kolom tersebut dan akan digunakan nanti untuk prediksi.

Modul pandas tersedia sebagai pd di ruang kerja Anda dan DataFrame contoh telah dimuat sebagai df.

Latihan ini adalah bagian dari kursus

Memprediksi CTR dengan Machine Learning di Python

Petunjuk latihan

Cetak jumlah total nilai dan jumlah nilai unik untuk setiap fitur dalam daftar feature_list.
Buat fitur baru dari fitur dalam new_feature_list dengan menghitung jumlah klik untuk setiap fitur menggunakan .transform().

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Get counts of total and unique values for given features
feature_list = ["search_engine_type", "product_type", "advertiser_type"]
for feature in feature_list:
	print(df[feature].____)
	print(df[feature].____)

# Define new features as counts
new_feature_list = ['device_id', 'site_id'] + feature_list
for new_feature in new_feature_list:
  df[new_feature + '_count'] = df.____(
    new_feature)['click'].____("count")
print(df.head(5))

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Memprediksi CTR dengan Machine Learning di Python

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.

Exercise 1: Introduction to click-through rates Exercise 2: Beginning steps Exercise 3: Feature exploration Exercise 4: First evaluation of data Exercise 5: Overview of machine learning models Exercise 6: Logistic regression for breast cancer Exercise 7: Logistic regression for images Exercise 8: A second toy model Exercise 9: CTR prediction using decision trees Exercise 10: Model implementation Exercise 11: A first CTR model Exercise 12: Beyond only accuracy

This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.

Exercise 1: Analisis data eksploratif Exercise 2: Tinjauan awal Exercise 3: Memeriksa nilai yang hilang Exercise 4: Distribusi menurut CTR Exercise 5: Rekayasa fitur Exercise 6: Menganalisis kolom datetime Exercise 7: Mengonversi variabel kategorikal Exercise 8: Membuat fitur baru

Latihan Saat Ini

Exercise 9: Menstandarkan fitur Exercise 10: Normalisasi log Exercise 11: Memahami standardisasi Exercise 12: Standard scaling

It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.

Exercise 1: Applications of metric evaluation Exercise 2: Four categories of outcomes Exercise 3: Evaluating four categories Exercise 4: ROI on ad spend Exercise 5: Model evaluation Exercise 6: Precision and recall Exercise 7: Baseline Exercise 8: Classifier comparison Exercise 9: Tuning models Exercise 10: Regularization Exercise 11: Cross validation Exercise 12: Model selection Exercise 13: Ensembles and hyperparameter tuning Exercise 14: Understanding hyperparameter tuning Exercise 15: Random forests Exercise 16: Grid search

Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.

Exercise 1: Introduction to deep learning Exercise 2: Understanding MLPs Exercise 3: Beginning model Exercise 4: MLPs for CTR Exercise 5: Hyperparameter tuning in deep learning Exercise 6: Hyperparameter tuning in MLPs Exercise 7: Varying hyperparameters Exercise 8: MLP Grid Search Exercise 9: Model evaluation Exercise 10: F-beta score Exercise 11: Low precision and high AUC Exercise 12: Precision, ROI, and AUC Exercise 13: Model review and comparison Exercise 14: Model comparison warmup Exercise 15: Evaluating precision and ROI Exercise 16: Total scoring Exercise 17: Wrap-up video