ROC AUC

La curva ROC che hai tracciato nell'ultimo esercizio sembrava promettente.

Ora calcolerai l'area sotto la curva ROC, insieme alle altre metriche di classificazione che hai usato in precedenza.

Le funzioni confusion_matrix e classification_report sono state precaricate per te, insieme al modello logreg che hai costruito in precedenza, oltre a X_train, X_test, y_train, y_test. Inoltre, le etichette del test set previste dal modello sono memorizzate come y_pred e le probabilità delle osservazioni del test set appartenenti alla classe positiva sono memorizzate come y_pred_probs.

È stato creato anche un modello knn e le metriche delle prestazioni sono state stampate nella console, in modo da poter confrontare roc_auc_score, confusion_matrix e classification_report tra i due modelli.

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

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Istruzioni dell'esercizio

Importazione roc_auc_score.
Calcola e stampa il punteggio ROC AUC, passando le etichette del test e le probabilità di classe positiva previste.
Calcola e stampa la matrice di confusione.
Chiama classification_report().

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import roc_auc_score
____

# Calculate roc_auc_score
print(____(____, ____))

# Calculate the confusion matrix
print(____(____, ____))

# Calculate the classification report
print(____(____, ____))

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Apprendimento supervisionato con scikit-learn

IntermediárioNível de habilidade

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In this chapter, you'll be introduced to classification problems and learn how to solve them using supervised learning techniques. You'll learn how to split data into training and test sets, fit a model, make predictions, and evaluate accuracy. You’ll discover the relationship between model complexity and performance, applying what you learn to a churn dataset, where you will classify the churn status of a telecom company's customers.

Exercise 1: Machine learning with scikit-learn Exercise 2: Binary classification Exercise 3: The supervised learning workflow Exercise 4: The classification challenge Exercise 5: k-Nearest Neighbors: Fit Exercise 6: k-Nearest Neighbors: Predict Exercise 7: Measuring model performance Exercise 8: Train/test split + computing accuracy Exercise 9: Overfitting and underfitting Exercise 10: Visualizing model complexity

In this chapter, you will be introduced to regression, and build models to predict sales values using a dataset on advertising expenditure. You will learn about the mechanics of linear regression and common performance metrics such as R-squared and root mean squared error. You will perform k-fold cross-validation, and apply regularization to regression models to reduce the risk of overfitting.

Exercise 1: Introduction to regression Exercise 2: Creating features Exercise 3: Building a linear regression model Exercise 4: Visualizing a linear regression model Exercise 5: The basics of linear regression Exercise 6: Fit and predict for regression Exercise 7: Regression performance Exercise 8: Cross-validation Exercise 9: Cross-validation for R-squared Exercise 10: Analyzing cross-validation metrics Exercise 11: Regularized regression Exercise 12: Regularized regression: Ridge Exercise 13: Lasso regression for feature importance

Having trained models, now you will learn how to evaluate them. In this chapter, you will be introduced to several metrics along with a visualization technique for analyzing classification model performance using scikit-learn. You will also learn how to optimize classification and regression models through the use of hyperparameter tuning.

Exercise 1: Quanto è bello il tuo modello?Exercise 2: Decidere una metrica primaria Exercise 3: Valutazione di un classificatore di previsione del diabete Exercise 4: Regressione logistica e curva ROC Exercise 5: Costruire un modello di regressione logistica Exercise 6: La curva ROC Exercise 7: ROC AUC

Esercizio in corso

Exercise 8: Regolazione dell'iperparametro Exercise 9: Regolazione degli iperparametri con GridSearchCV Exercise 10: Sintonizzazione degli iperparametri con RandomizedSearchCV

Learn how to impute missing values, convert categorical data to numeric values, scale data, evaluate multiple supervised learning models simultaneously, and build pipelines to streamline your workflow!

Exercise 1: Preprocessing data Exercise 2: Creating dummy variables Exercise 3: Regression with categorical features Exercise 4: Handling missing data Exercise 5: Dropping missing data Exercise 6: Pipeline for song genre prediction: I Exercise 7: Pipeline for song genre prediction: II Exercise 8: Centering and scaling Exercise 9: Centering and scaling for regression Exercise 10: Centering and scaling for classification Exercise 11: Evaluating multiple models Exercise 12: Visualizing regression model performance Exercise 13: Predicting on the test set Exercise 14: Visualizing classification model performance Exercise 15: Pipeline for predicting song popularity Exercise 16: Congratulations