Random forest

Le Random Forest sono un metodo di ensemble classico e potente che sfrutta singoli alberi decisionali tramite bootstrap aggregation (o, in breve, bagging). I due principali iperparametri di questo tipo di modello sono il numero di alberi e la profondità massima di ciascun albero. In questo esercizio, implementerai e valuterai un semplice classificatore random forest con alcuni valori di iperparametri fissati.

X_train, y_train, X_test, y_test sono disponibili nel tuo workspace. pandas come pd, numpy come np e sklearn sono anch'essi disponibili. RandomForestClassifier() da sklearn.ensemble è disponibile, insieme a roc_curve() e auc() da sklearn.metrics.

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Prevedere il CTR con il Machine Learning in Python

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Istruzioni dell'esercizio

Crea un classificatore random forest con 50 alberi e una profondità massima di 5.
Addestra il classificatore e ottieni i punteggi di probabilità tramite .predict_proba() e le predizioni tramite .predict() sui dati di test.
Valuta l'AUC della curva ROC per il classificatore usando prima roc_curve() per calcolare fpr e tpr, quindi auc() sul risultato.
Valuta precision e recall per il classificatore.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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))

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Prevedere il CTR con il Machine Learning in Python

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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: Applicazioni della valutazione delle metriche Exercise 2: Quattro categorie di esiti Exercise 3: Valutare quattro categorie Exercise 4: ROI sulla spesa pubblicitaria Exercise 5: Valutazione del modello Exercise 6: Precision e recall Exercise 7: Baseline Exercise 8: Confronto tra classificatori Exercise 9: Ottimizzazione dei modelli Exercise 10: Regolarizzazione Exercise 11: Cross validation Exercise 12: Selezione del modello Exercise 13: Ensemble e ottimizzazione degli iperparametri Exercise 14: Capire l’hyperparameter tuning Exercise 15: Random forest

Esercizio in corso

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