ROC AUC

Önceki egzersizde çizdiğin ROC eğrisi umut vericiydi.

Şimdi ROC eğrisinin altındaki alanı (AUC) ve daha önce kullandığın diğer sınıflandırma metriklerini hesaplayacaksın.

confusion_matrix ve classification_report fonksiyonları, daha önce oluşturduğun logreg modeliyle birlikte senin için yüklendi; ayrıca X_train, X_test, y_train, y_test de hazır. Modelin test kümesi için tahmin ettiği etiketler y_pred değişkeninde, gözlemlerin pozitif sınıfa ait olma olasılıkları ise y_pred_probs içinde tutuluyor.

Ayrıca bir knn modeli de oluşturuldu ve performans metrikleri konsola yazdırıldı; böylece iki model arasında roc_auc_score, confusion_matrix ve classification_report çıktılarının karşılaştırmasını yapabilirsin.

Bu egzersiz

scikit-learn ile Supervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

roc_auc_score fonksiyonunu içe aktar.
Test etiketlerini ve pozitif sınıf olasılıklarını vererek ROC AUC skorunu hesapla ve yazdır.
Karmaşıklık matrisini (confusion matrix) hesapla ve yazdır.
classification_report() fonksiyonunu çağır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import roc_auc_score
____

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

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

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

scikit-learn ile Supervised Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Modelin ne kadar iyi?Exercise 2: Birincil metriğe karar verme Exercise 3: Diyabet tahmin sınıflandırıcısını değerlendirme Exercise 4: Lojistik regresyon ve ROC eğrisi Exercise 5: Lojistik regresyon modeli oluşturma Exercise 6: ROC eğrisi Exercise 7: ROC AUC

Geçerli Egzersiz

Exercise 8: Hiperparametre ayarı Exercise 9: GridSearchCV ile hiperparametre ayarlama Exercise 10: RandomizedSearchCV ile hiperparametre ayarlama

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