Penilaian total

Ingat bahwa precision dan recall dapat diberi bobot berbeda sehingga skor F-beta menjadi metrik evaluasi yang penting. Selain itu, ROC dari kurva AUC merupakan metrik pelengkap yang penting bagi precision dan recall karena sebelumnya Anda melihat bahwa sebuah model bisa saja memiliki AUC tinggi tetapi precision rendah. Dalam latihan ini, Anda akan menghitung serangkaian metrik evaluasi lengkap untuk setiap classifier.

Fungsi print_estimator_name() disediakan untuk menampilkan nama setiap classifier. X_train, y_train, X_test, y_test tersedia di workspace Anda, dan fitur-fitur telah distandardisasi. pandas sebagai pd dan sklearn juga tersedia di workspace Anda.

Latihan ini adalah bagian dari kursus

Memprediksi CTR dengan Machine Learning di Python

Petunjuk latihan

Definisikan classifier MLP dengan satu hidden layer berisi 10 unit tersembunyi dan 50 iterasi maksimum.
Latih dan lakukan prediksi untuk setiap classifier.
Gunakan implementasi dari sklearn untuk memperoleh precision, recall, skor F-beta, dan AUC dari skor ROC.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create classifiers
clfs = [LogisticRegression(), DecisionTreeClassifier(), RandomForestClassifier(), 
        ____(____ = (10, ), ____ = 50)]

# Produce all evaluation metrics for each classifier
for clf in clfs:
  print("Evaluating classifier: %s" %(print_estimator_name(clf)))
  y_score = clf.fit(X_train, y_train).____(X_test)
  y_pred = clf.fit(X_train, y_train).____(X_test)
  prec = ____(y_test, y_pred, average = 'weighted')
  recall = ____(y_test, y_pred, average = 'weighted')
  fbeta = ____(y_test, y_pred, beta = 0.5, average = 'weighted')
  roc_auc = ____(y_test, y_score[:, 1])
  print("Precision: %s: Recall: %s, F-beta score: %s, AUC of ROC curve: %s" 
        %(prec, recall, fbeta, 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: 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: 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: Pengantar deep learning Exercise 2: Memahami MLP Exercise 3: Model awal Exercise 4: MLP untuk CTR Exercise 5: Penyetelan hyperparameter dalam deep learning Exercise 6: Penyetelan hiperparameter pada MLP Exercise 7: Memvariasikan hyperparameter Exercise 8: Grid Search MLP Exercise 9: Evaluasi model Exercise 10: Skor F-beta Exercise 11: Presisi rendah dan AUC tinggi Exercise 12: Precision, ROI, dan AUC Exercise 13: Tinjauan dan perbandingan model Exercise 14: Pemanasan perbandingan model Exercise 15: Mengevaluasi precision dan ROI Exercise 16: Penilaian total

Latihan Saat Ini

Exercise 17: Video penutup