2B noktaları kümeleme

Önceki egzersizdeki saçılım grafiğinden, noktaların 3 kümeye ayrılıyor gibi göründüğünü gördün. Şimdi 3 küme bulmak için bir KMeans modeli oluşturacak ve onu önceki egzersizdeki veri noktalarına uyduracaksın. Modeli uydurduktan sonra, .predict() metodunu kullanarak bazı yeni noktaların küme etiketlerini elde edeceksin.

Sana önceki egzersizden points dizisi ve ayrıca bir new_points dizisi verildi.

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

sklearn.cluster içinden KMeans'i içe aktar.
KMeans() kullanarak, 3 küme bulacak model adlı bir KMeans örneği oluştur. Küme sayısını belirtmek için n_clusters anahtar argümanını kullan.
modelin .fit() metodunu kullanarak modeli points dizisine uydur.
modelin .predict() metodunu kullanarak new_points'un küme etiketlerini tahmin et ve sonucu labels değişkenine ata.
new_points'un küme etiketlerini görmek için gönder tuşuna bas.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import KMeans
____

# Create a KMeans instance with 3 clusters: model
model = ____

# Fit model to points
____

# Determine the cluster labels of new_points: labels
labels = ____

# Print cluster labels of new_points
print(labels)

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

Geçerli Egzersiz

Exercise 4: Kümelemeni incele Exercise 5: Bir kümelenmeyi değerlendirme Exercise 6: Kaç tahıl kümesi var?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