Model karmaşıklığını görselleştirme

Artık n_neighbors için farklı değerler kullanarak KNN modelinin eğitim ve test setlerindeki doğruluğunu hesapladığına göre, model daha az karmaşık hale geldikçe performansın nasıl değiştiğini görmek için bir model karmaşıklığı eğrisi oluşturabilirsin!

Önceki egzersizde oluşturduğun neighbors, train_accuracies ve test_accuracies değişkenlerinin tümü senin için önceden yüklendi. Modelin için en uygun komşu sayısını bulmana yardımcı olması için sonuçları çizeceksin.

Bu egzersiz

scikit-learn ile Supervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

"KNN: Varying Number of Neighbors" başlığını ekle.
train_accuracies'in .values() metodunu y ekseninde, neighbors'ı x ekseninde olacak şekilde "Training Accuracy" etiketiyle çiz.
test_accuracies'in .values() metodunu y ekseninde, neighbors'ı x ekseninde olacak şekilde "Testing Accuracy" etiketiyle çiz.
Grafiği görüntüle.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Add a title
plt.title("____")

# Plot training accuracies
plt.plot(____, ____, label="____")

# Plot test accuracies
plt.plot(____, ____, label="____")

plt.legend()
plt.xlabel("Number of Neighbors")
plt.ylabel("Accuracy")

# Display the plot
____

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: scikit-learn ile Machine Learning Exercise 2: İkili sınıflandırma Exercise 3: Supervised learning iş akışı Exercise 4: Sınıflandırma meydan okuması Exercise 5: k-En Yakın Komşular: Modeli Uydur Exercise 6: k-En Yakın Komşu: Tahmin Exercise 7: Model performansını ölçmek Exercise 8: Eğitim/test ayrımı + doğruluk hesaplama Exercise 9: Aşırı uyum ve yetersiz uyum Exercise 10: Model karmaşıklığını görselleştirme

Geçerli Egzersiz

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: 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