Variare gli iperparametri

Il numero di iterazioni di training e la dimensione dei livelli nascosti sono due iperparametri principali che puoi variare quando lavori con un classificatore MLP. In questo esercizio li varierai separatamente e osserverai come cambiano le prestazioni in termini di accuracy e AUC della curva ROC.

X_train, y_train, X_test, y_test sono disponibili nel tuo workspace. Le feature sono già state standardizzate usando StandardScaler(). pandas come pd e numpy come np sono anch’essi disponibili nel tuo workspace.

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Prevedere il CTR con il Machine Learning in Python

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Loop over various max_iter configurations
max_iter_list = [10, 20, 30]
for max_iter in ____:
	clf = MLPClassifier(hidden_layer_sizes = (4, ), 
                        ____ = max_iter, random_state = 0)
   	# Extract relevant predictions
	y_score = clf.fit(____, ____).____(X_test)
	y_pred = clf.fit(____, ____).____(X_test)

	# Get ROC curve metrics
	print("Accuracy for max_iter = %s: %s" %(
      max_iter, _____(y_test, ____)))
	print("AUC for max_iter = %s: %s" %(
      max_iter, ____(y_test, ____[:, 1])))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

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: 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: Introduzione al deep learning Exercise 2: Capire gli MLP Exercise 3: Modello iniziale Exercise 4: MLP per il CTR Exercise 5: Ottimizzazione degli iperparametri nel deep learning Exercise 6: Ottimizzazione degli iperparametri negli MLP Exercise 7: Variare gli iperparametri

Esercizio in corso

Exercise 8: Grid search per MLP Exercise 9: Valutazione del modello Exercise 10: Punteggio F-beta Exercise 11: Bassa precision e AUC alta Exercise 12: Precision, ROI e AUC Exercise 13: Revisione e confronto dei modelli Exercise 14: Riscaldamento al confronto tra modelli Exercise 15: Valutare precision e ROI Exercise 16: Valutazione completa Exercise 17: Video di riepilogo