Regresyon performansı

Artık sales_df içindeki tüm özellikleri kullanarak bir model (reg) eğittin ve satış değerleri için tahminler yaptın; şimdi performansı yaygın regresyon metrikleriyle değerlendirebilirsin.

Son egzersizden gelen X_train, X_test, y_train, y_test ve y_pred değişkenleri ile eğitilmiş model reg senin için önceden yüklendi.

Görevin, özelliklerin hedef değerlerdeki varyansı ne kadar iyi açıkladığını bulmak ve modelin görülmemiş veriler üzerinde tahmin yapma başarısını değerlendirmek.

Bu egzersiz

scikit-learn ile Supervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

root_mean_squared_error fonksiyonunu içe aktar.
Uygun bir yönteme test özellik değerlerini ve test hedef değerlerini vererek modelin R-kare skorunu hesapla.
y_test ve y_pred kullanarak modelin kök ortalama kare hatasını (RMSE) hesapla.
r_squared ve rmse değerlerini yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import root_mean_squared_error
from ____.____ import ____

# Compute R-squared
r_squared = reg.____(____, ____)

# Compute RMSE
rmse = ____(____, ____)

# Print the metrics
print("R^2: {}".format(____))
print("RMSE: {}".format(____))

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: Regresyona giriş Exercise 2: Özellik oluşturma Exercise 3: Bir doğrusal regresyon modeli kurma Exercise 4: Doğrusal regresyon modelini görselleştirme Exercise 5: Doğrusal regresyonun temelleri Exercise 6: Regresyon için fit ve predict Exercise 7: Regresyon performansı

Geçerli Egzersiz

Exercise 8: Çapraz doğrulama Exercise 9: R-kare için çapraz doğrulama Exercise 10: Çapraz doğrulama metriklerini analiz etme Exercise 11: Düzenlileştirilmiş regresyon Exercise 12: Düzenlileştirilmiş regresyon: Ridge Exercise 13: Özellik önemini belirlemek için lasso regresyonu

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: How good is your model?Exercise 2: Deciding on a primary metric Exercise 3: Assessing a diabetes prediction classifier Exercise 4: Logistic regression and the ROC curve Exercise 5: Building a logistic regression model Exercise 6: The ROC curve Exercise 7: ROC AUC Exercise 8: Hyperparameter tuning Exercise 9: Hyperparameter tuning with GridSearchCV Exercise 10: Hyperparameter tuning with 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