Kaç tahıl kümesi var?

Videoda, bir veri kümesi için iyi bir küme sayısını k-means atalet grafiğini kullanarak nasıl seçeceğini öğrendin. Sana, tahıl örneklerinin ölçümlerini (örneğin alan, çevre, uzunluk ve diğer bazıları) içeren samples dizisi verildi. Bu durumda iyi bir küme sayısı nedir?

KMeans ve PyPlot (plt) senin için zaten içe aktarıldı.

Bu veri kümesi UCI Machine Learning Repository kaynağından alınmıştır.

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Verilen her k değeri için şu adımları uygula:
k küme ile model adlı bir KMeans örneği oluştur.
Modeli tahıl verisi samples üzerine uydur.
model nesnesinin inertia_ özniteliğinin değerini inertias listesine ekle.
ks ile inertias değerlerini çizen kod senin için yazıldı, grafiği görmek için Gönder’e bas!

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

ks = range(1, 6)
inertias = []

for k in ks:
    # Create a KMeans instance with k clusters: model
    ____
    
    # Fit model to samples
    ____
    
    # Append the inertia to the list of inertias
    ____
    
# Plot ks vs inertias
plt.plot(ks, inertias, '-o')
plt.xlabel('number of clusters, k')
plt.ylabel('inertia')
plt.xticks(ks)
plt.show()

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: Kaç küme var?Exercise 3: 2B noktaları kümeleme Exercise 4: Kümelemeni incele Exercise 5: Bir kümelenmeyi değerlendirme Exercise 6: Kaç tahıl kümesi var?

Geçerli Egzersiz

Exercise 7: Tahıl kümelemesini değerlendirme Exercise 8: Daha iyi kümelenmeler için özellikleri dönüştürme Exercise 9: Kümelenme için balık verilerini ölçekleme Exercise 10: Balık verilerini kümeleme Exercise 11: KMeans ile hisseleri kümeleme Exercise 12: Hangi hisseler birlikte hareket ediyor?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizing hierarchies Exercise 2: How many merges?Exercise 3: Hierarchical clustering of the grain data Exercise 4: Hierarchies of stocks Exercise 5: Cluster labels in hierarchical clustering Exercise 6: Which clusters are closest?Exercise 7: Different linkage, different hierarchical clustering!Exercise 8: Intermediate clusterings Exercise 9: Extracting the cluster labels Exercise 10: t-SNE for 2-dimensional maps Exercise 11: t-SNE visualization of grain dataset Exercise 12: A t-SNE map of the stock market

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Non-negative matrix factorization (NMF)Exercise 2: Non-negative data Exercise 3: NMF applied to Wikipedia articles Exercise 4: NMF features of the Wikipedia articles Exercise 5: NMF reconstructs samples Exercise 6: NMF learns interpretable parts Exercise 7: NMF learns topics of documents Exercise 8: Explore the LED digits dataset Exercise 9: NMF learns the parts of images Exercise 10: PCA doesn't learn parts Exercise 11: Building recommender systems using NMF Exercise 12: Which articles are similar to 'Cristiano Ronaldo'?Exercise 13: Recommend musical artists part I Exercise 14: Recommend musical artists part II Exercise 15: Final thoughts