Melampaui sekadar akurasi

Dalam latihan ini, untuk melampaui sekadar melihat akurasi, Anda akan mengevaluasi AUC dari kurva ROC untuk model decision tree dasar. Ingat bahwa pembanding dasar untuk pengklasifikasi acak adalah AUC sebesar 0,5, sehingga Anda sebaiknya meraih skor lebih tinggi daripada 0,5.

X tersedia sebagai DataFrame berisi fitur, dan y tersedia sebagai DataFrame berisi nilai target. sklearn dan pandas sebagai pd juga tersedia di ruang kerja Anda.

Kita akan menggunakan susunan ini untuk melihat AUC dari kurva ROC kita.

Latihan ini adalah bagian dari kursus

Memprediksi CTR dengan Machine Learning di Python

Petunjuk latihan

Bagi data menjadi himpunan pelatihan dan pengujian.
Latih pengklasifikasi menggunakan data pelatihan untuk membuat prediksi pada data pengujian menggunakan predict_proba() dan predict().
Evaluasi AUC di bawah kurva ROC menggunakan fungsi roc_curve() pada y_test melalui roc_curve(y_test, y_score[:, 1]).

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Training and testing
X_train, X_test, y_train, y_test = \
	____(X, y, test_size = .2, random_state = 0)

# Create decision tree classifier
clf = DecisionTreeClassifier()

# Train classifier - predict probability score and label
y_score = clf.fit(____, ____).predict_proba(____) 
y_pred = clf.fit(____, ____).predict(____) 

# Get ROC curve metrics
fpr, tpr, thresholds = ____(____, y_score[:, 1])
roc_auc = auc(fpr, tpr)
print(roc_auc)

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: Pendahuluan tentang click-through rate Exercise 2: Langkah awal Exercise 3: Eksplorasi fitur Exercise 4: Evaluasi awal data Exercise 5: Gambaran umum model Machine Learning Exercise 6: Regresi logistik untuk kanker payudara Exercise 7: Regresi logistik untuk citra Exercise 8: Model mainan kedua Exercise 9: Prediksi CTR menggunakan decision tree Exercise 10: Implementasi model Exercise 11: Model CTR pertama Exercise 12: Melampaui sekadar akurasi

Latihan Saat Ini

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: Exploratory data analysis Exercise 2: A first look Exercise 3: Checking for missing values Exercise 4: Distributions by CTR Exercise 5: Feature engineering Exercise 6: Analyzing datetime columns Exercise 7: Converting categorical variables Exercise 8: Creating new features Exercise 9: Standardizing features Exercise 10: Log normalization Exercise 11: Understanding standardization 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