Random forest

Random Forest adalah metode ensemble klasik dan kuat yang memanfaatkan pohon keputusan individual melalui bootstrap aggregation (atau disingkat bagging). Dua hiperparameter utama dalam jenis model ini adalah jumlah pohon dan kedalaman maksimum setiap pohon. Pada latihan ini, Anda akan menerapkan dan mengevaluasi sebuah classifier random forest sederhana dengan beberapa nilai hiperparameter yang sudah ditetapkan.

X_train, y_train, X_test, y_test tersedia di workspace Anda. pandas sebagai pd, numpy sebagai np, dan sklearn juga tersedia di workspace Anda. RandomForestClassifier() dari sklearn.ensemble tersedia, begitu pula roc_curve() dan auc() dari sklearn.metrics.

Latihan ini adalah bagian dari kursus

Memprediksi CTR dengan Machine Learning di Python

Petunjuk latihan

Buat classifier random forest dengan 50 pohon, dan kedalaman maksimum 5.
Latih classifier dan peroleh skor probabilitas melalui .predict_proba(), serta prediksi melalui .predict() untuk data pengujian.
Evaluasi AUC dari kurva ROC untuk classifier dengan terlebih dahulu menggunakan roc_curve() untuk menghitung fpr dan tpr, lalu auc() pada hasilnya.
Evaluasi precision dan recall untuk classifier.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create random forest classifier with specified params
clf = ____(____ = 50, ____ = 5)

# Train classifier - predict probability score and label
y_score = clf.____(X_train, y_train).____(X_test) 
y_pred = clf.____(X_train, y_train).____(X_test) 

# Get ROC curve metrics
fpr, tpr, thresholds = ____(y_test, y_score[:, 1])
print("ROC of AUC: %s"%(____(fpr, tpr)))

# Get precision and recall
precision = ____(y_test, y_pred, average = 'weighted')
recall = ____(y_test, y_pred, average = 'weighted')
print("Precision: %s, Recall: %s" %(precision, recall))

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: Penerapan evaluasi metrik Exercise 2: Empat kategori keluaran Exercise 3: Evaluasi empat kategori Exercise 4: ROI pada belanja iklan Exercise 5: Evaluasi model Exercise 6: Precision dan recall Exercise 7: Garis Dasar Exercise 8: Perbandingan classifier Exercise 9: Melakukan tuning model Exercise 10: Regularisasi Exercise 11: Cross validation Exercise 12: Pemilihan model Exercise 13: Ansambel dan penyetelan hiperparameter Exercise 14: Memahami penyetelan hiperparameter Exercise 15: Random forest

Latihan Saat Ini

Exercise 16: Pencarian grid

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