Data berkorelasi di alam

Anda diberikan sebuah array grains yang berisi lebar dan panjang sampel biji-bijian. Anda menduga bahwa lebar dan panjang saling berkorelasi. Untuk mengonfirmasi hal ini, buatlah plot sebar lebar vs panjang dan ukur korelasi Pearson di antara keduanya.

Latihan ini adalah bagian dari kursus

Unsupervised Learning in Python

Petunjuk latihan

Impor:
- matplotlib.pyplot sebagai plt.
- pearsonr dari scipy.stats.
Tetapkan kolom 0 dari grains ke width dan kolom 1 dari grains ke length.
Buat plot sebar dengan width pada sumbu-x dan length pada sumbu-y.
Gunakan fungsi pearsonr() untuk menghitung korelasi Pearson dari width dan length.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Perform the necessary imports
____
____

# Assign the 0th column of grains: width
width = ____

# Assign the 1st column of grains: length
length = ____

# Scatter plot width vs length
plt.scatter(____, ____)
plt.axis('equal')
plt.show()

# Calculate the Pearson correlation
correlation, pvalue = ____

# Display the correlation
print(correlation)

Edit dan Jalankan Kode

Latihan ini adalah 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: 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: Memvisualisasikan transformasi PCA Exercise 2: Data berkorelasi di alam

Latihan Saat Ini

Exercise 3: Menghilangkan korelasi pengukuran biji dengan PCA Exercise 4: Komponen utama Exercise 5: Dimensi intrinsik Exercise 6: Komponen utama pertama Exercise 7: Varians dari fitur PCA Exercise 8: Dimensi intrinsik dari data ikan Exercise 9: Reduksi dimensi dengan PCA Exercise 10: Reduksi dimensi pada pengukuran ikan Exercise 11: Array frekuensi kata tf-idf Exercise 12: Pengelompokan Wikipedia bagian I Exercise 13: Mengelompokkan Wikipedia bagian 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