Regressione logistica per immagini

In questo esercizio costruirai un modello di regressione logistica su un insieme di dati sul cancro al seno dall'esercizio precedente e ne valuterai l'accuratezza.

Gli X_train, X_test, y_train e y_test che hai creato nell'esercizio precedente sono disponibili nel tuo workspace, insieme a sklearn e pandas come pd. LogisticRegression è disponibile in sklearn.linear_model.

Questo esercizio fa parte del corso

Prevedere il CTR con il Machine Learning in Python

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Istruzioni dell'esercizio

Crea un classificatore di regressione logistica.
Esegui il fit del classificatore sui dati di training per fare previsioni sui dati di test.
Assegna i totali delle previsioni corrette e errate tra i target di training e di test rispettivamente a num_right e num_wrong.
Calcola l'accuratezza usando num_right e num_wrong con sum().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Create and fit a logistic regression classifier
clf = ____().____(X_train, y_train)

# Predict labels
y_pred = clf.____(X_test) 

# Get number of right and wrong predictions
num_right = (y_test == y_pred).____()
num_wrong = (y_test != y_pred).____()

# Compute and print accuracy
accuracy = 1.0 * num_right / (____ + ____)
print(accuracy)

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: Introduzione ai click-through rate Exercise 2: Primi passi Exercise 3: Esplorazione delle feature Exercise 4: Prima valutazione dei dati Exercise 5: Panoramica dei modelli di Machine Learning Exercise 6: Regressione logistica per il tumore al seno Exercise 7: Regressione logistica per immagini

Esercizio in corso

Exercise 8: Un secondo modello di prova Exercise 9: Previsione del CTR con alberi decisionali Exercise 10: Implementazione del modello Exercise 11: Un primo modello di CTR Exercise 12: Oltre la sola accuratezza

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