Valutare quattro categorie

La matrice di confusione è lo strumento più diretto per osservare le quattro categorie di esiti: veri positivi (TP), falsi positivi (FP), veri negativi (TN) e falsi negativi (FN). In questo esercizio userai un classificatore ad albero decisionale standard DecisionTreeClassifier() di sklearn sui dati di esempio dei click e calcolerai la ripartizione degli esiti nelle quattro categorie.

Il modulo pandas è disponibile come pd nel tuo workspace e il DataFrame di esempio è caricato come df. Le feature sono caricate in X e il target è caricato in y per l’uso. Inoltre, DecisionTreeClassifier da sklearn.tree è disponibile.

Questo esercizio fa parte del corso

Prevedere il CTR con il Machine Learning in Python

Visualizza il corso

Istruzioni dell'esercizio

Ottieni le suddivisioni di training e test per X e y.
Definisci un classificatore ad albero decisionale e genera le previsioni y_pred addestrando il modello.
Usa la matrice di confusione per ottenere i conteggi per le categorie di ciascun esito, con 1 che indica un positivo (click) e 0 che indica un negativo (non click).
Ad esempio: i veri negativi corrispondono a [0,0] e i veri positivi a [1,1].

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Set up classifier using training data to predict test data
X_train, X_test, y_train, y_test = ____(
  X, y, test_size = .2, random_state = 0)
clf = ____
y_pred = clf.____(X_train, y_train).____(X_test) 

# Define confusion matrix and four categories
conf_matrix = ____(y_test, y_pred)
tn = conf_matrix[____][____]
fp = conf_matrix[____][____]
fn = conf_matrix[____][____]
tp = conf_matrix[____][____]

print("TN: %s, FP: %s, FN: %s, TP: %s" %(tn, fp, fn, tp))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Prevedere il CTR con il Machine Learning in Python

IntermediárioNível de habilidade

4.9+

Inizia il corso 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: Applicazioni della valutazione delle metriche Exercise 2: Quattro categorie di esiti Exercise 3: Valutare quattro categorie

Esercizio in corso

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 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