Gecorreleerde data in de natuur

Je krijgt een array grains met de breedte en lengte van graanmonsters. Je vermoedt dat breedte en lengte gecorreleerd zijn. Bevestig dit door een scatterplot van breedte versus lengte te maken en hun Pearson-correlatie te meten.

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Oefeninstructies

Importeer:
- matplotlib.pyplot als plt.
- pearsonr uit scipy.stats.
Wijs kolom 0 van grains toe aan width en kolom 1 van grains aan length.
Maak een scatterplot met width op de x-as en length op de y-as.
Gebruik de functie pearsonr() om de Pearson-correlatie van width en length te berekenen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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)

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Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

SkillTag.level.intermediateSkillTag.label

4.8+

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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: De PCA-transformatie visualiseren Exercise 2: Gecorreleerde data in de natuur

Huidige oefening

Exercise 3: Met PCA de korrelmetingen decorreleren Exercise 4: Hoofdcomponenten Exercise 5: Intrinsieke dimensie Exercise 6: De eerste hoofcomponent Exercise 7: Variantie van de PCA-features Exercise 8: Intrinsieke dimensie van de visgegevens Exercise 9: Dimensiereductie met PCA Exercise 10: Dimensiereductie van de vismetingen Exercise 11: Een tf-idf-woordfrequentie-array Exercise 12: Wikipedia clusteren, deel I Exercise 13: Wikipedia clusteren, deel 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