KNN'nin Hiperparametreleri

Önceki egzersizde öğrendiğin kavramları pekiştirmek için bunları yeni bir algoritmada denemek iyi bir pratiktir. k-en yakın komşu (k-nearest neighbors) algoritması eskisi kadar popüler olmasa da, benzer şekilde davranan gruplar içeren veri kümelerinde hâlâ mükemmel bir seçenek olabilir. Kredi kartı kullanıcılarımız için de durum böyle olabilir mi?

Bu egzersizde knn algoritmasının temel hiperparametrelerinden biri için birkaç farklı değeri deneyecek ve performanslarını karşılaştıracaksın.

Elinde şunlar olacak:

X_train, X_test, y_train, y_test DataFrame'leri

Bu egzersiz

Python'da Hiperparametre Ayarlama

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

n_neighbors için [5,10,20] değerleriyle bir knn tahminleyicisi kur.
Her birini eğitim verisine uydur ve tahminler üret.
Her model için bir doğruluk skoru al ve yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Build a knn estimator for each value of n_neighbours
knn_5 = KNeighborsClassifier(n_neighbors=____)
knn_10 = KNeighborsClassifier(n_neighbors=____)
knn_20 = KNeighborsClassifier(n_neighbors=____)

# Fit each to the training data & produce predictions
knn_5_predictions = knn_5.fit(_____, _____).predict(_____)
knn_10_predictions = knn_10.fit(_____, _____).predict(_____)
knn_20_predictions = knn_20.fit(_____, _____).predict(_____)

# Get an accuracy score for each of the models
knn_5_accuracy = accuracy_score(y_test, _____)
knn_10_accuracy = accuracy_score(y_test, _____)
knn_20_accuracy = accuracy_score(y_test, _____)
print("The accuracy of 5, 10, 20 neighbours was {}, {}, {}".format(_____, _____, _____))

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Hiperparametre Ayarlama

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices.

Exercise 1: Giriş ve 'Parametreler'Exercise 2: Lojistik Regresyonda Parametreler Exercise 3: Lojistik Regresyon parametresi çıkarma Exercise 4: Random Forest bir parametreyi çıkarmak Exercise 5: Hiperparametrelere Giriş Exercise 6: Random Forest'larda Hiperparametreler Exercise 7: Random Forest Hiperparametrelerini Keşfetme Exercise 8: KNN'nin Hiperparametreleri

Geçerli Egzersiz

Exercise 9: Hiperparametre Değerlerini Ayarlama ve Analiz Etme Exercise 10: Hiperparametre Seçimini Otomatikleştirme Exercise 11: Öğrenme Eğrileri Oluşturma

This chapter introduces you to a popular automated hyperparameter tuning methodology called Grid Search. You will learn what it is, how it works and practice undertaking a Grid Search using Scikit Learn. You will then learn how to analyze the output of a Grid Search & gain practical experience doing this.

Exercise 1: Introducing Grid Search Exercise 2: Build Grid Search functions Exercise 3: Iteratively tune multiple hyperparameters Exercise 4: How Many Models?Exercise 5: Grid Search with Scikit Learn Exercise 6: GridSearchCV inputs Exercise 7: GridSearchCV with Scikit Learn Exercise 8: Understanding a grid search output Exercise 9: Using the best outputs Exercise 10: Exploring the grid search results Exercise 11: Analyzing the best results Exercise 12: Using the best results

In this chapter you will be introduced to another popular automated hyperparameter tuning methodology called Random Search. You will learn what it is, how it works and importantly how it differs from grid search. You will learn some advantages and disadvantages of this method and when to choose this method compared to Grid Search. You will practice undertaking a Random Search with Scikit Learn as well as visualizing & interpreting the output.

Exercise 1: Introducing Random Search Exercise 2: Randomly Sample Hyperparameters Exercise 3: Randomly Search with Random Forest Exercise 4: Visualizing a Random Search Exercise 5: Random Search in Scikit Learn Exercise 6: RandomSearchCV inputs Exercise 7: The RandomizedSearchCV Object Exercise 8: RandomSearchCV in Scikit Learn Exercise 9: Comparing Grid and Random Search Exercise 10: Comparing Random & Grid Search Exercise 11: Grid and Random Search Side by Side

In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go.

Exercise 1: Informed Search: Coarse to Fine Exercise 2: Visualizing Coarse to Fine Exercise 3: Coarse to Fine Iterations Exercise 4: Informed Search: Bayesian Statistics Exercise 5: Bayes Rule in Python Exercise 6: Bayesian Hyperparameter tuning with Hyperopt Exercise 7: Informed Search: Genetic Algorithms Exercise 8: Genetic Hyperparameter Tuning with TPOT Exercise 9: Analysing TPOT's stability Exercise 10: Congratulations!