Mengekstrak label klaster

Pada latihan sebelumnya, Anda melihat bahwa pengelompokan sementara sampel gandum pada ketinggian 6 memiliki 3 klaster. Sekarang, gunakan fungsi fcluster() untuk mengekstrak label klaster dari pengelompokan sementara ini, lalu bandingkan label tersebut dengan varietas gandum menggunakan tabulasi silang.

Hierarchical clustering telah dilakukan dan mergings adalah hasil dari fungsi linkage(). Daftar varieties memberikan varietas untuk setiap sampel gandum.

Latihan ini merupakan bagian dari kursus

Unsupervised Learning in Python

Instruksi latihan

Impor:
- pandas sebagai pd.
- fcluster dari scipy.cluster.hierarchy.
Lakukan flat hierarchical clustering dengan menggunakan fungsi fcluster() pada mergings. Tentukan tinggi maksimum 6 dan argumen kata kunci criterion='distance'.
Buat DataFrame df dengan dua kolom bernama 'labels' dan 'varieties', masing-masing menggunakan labels dan varieties sebagai nilai kolom. Ini telah disiapkan untuk Anda.
Buat tabulasi silang ct antara df['labels'] dan df['varieties'] untuk menghitung frekuensi setiap varietas gandum pada setiap label klaster.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

# Perform the necessary imports
import ____ as ____
from ____ import ____

# Use fcluster to extract labels: labels
labels = ____

# Create a DataFrame with labels and varieties as columns: df
df = pd.DataFrame({'labels': labels, 'varieties': varieties})

# Create crosstab: ct
ct = ____

# Display ct
print(ct)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus Gratis

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: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

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: Memvisualisasikan hierarki Exercise 2: Berapa banyak penggabungan?Exercise 3: Hierarchical clustering pada data biji-bijian Exercise 4: Hierarki saham Exercise 5: Label klaster dalam hierarchical clustering Exercise 6: Klaster mana yang paling dekat?Exercise 7: Linkage berbeda, hierarchical clustering berbeda!Exercise 8: Pengelompokan menengah Exercise 9: Mengekstrak label klaster

Latihan Saat Ini

Exercise 10: t-SNE untuk peta 2 dimensi Exercise 11: Visualisasi t-SNE pada himpunan data biji-bijian Exercise 12: Peta t-SNE pasar saham

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