Dimensiereductie van de vismetingen

In een eerdere oefening zag je dat 2 een redelijke keuze was voor de "intrinsieke dimensie" van de vismetingen. Gebruik nu PCA voor dimensiereductie van de vismetingen en behoud alleen de 2 belangrijkste componenten.

De vismetingen zijn al voor je geschaald en zijn beschikbaar als scaled_samples.

Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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Oefeninstructies

Importeer PCA uit sklearn.decomposition.
Maak een PCA-instantie met de naam pca met n_components=2.
Gebruik de methode .fit() van pca om te fitten op de geschaalde vismetingen scaled_samples.
Gebruik de methode .transform() van pca om de scaled_samples te transformeren. Sla het resultaat op in pca_features.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import PCA
____

# Create a PCA model with 2 components: pca
pca = ____

# Fit the PCA instance to the scaled samples
____

# Transform the scaled samples: pca_features
pca_features = ____

# Print the shape of pca_features
print(pca_features.shape)

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Deze oefening maakt deel uit van de cursus

Unsupervised Learning in Python

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

Huidige oefening

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